📄 A Review of Large Language Models and Autonomous Agents in Chemistry

Paper · 2024 · Mayk Caldas Ramos, Christopher J. Collison and Andrew D. White

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

🏛️ Institutions

🎓University of RochesterRochester · United States36 works · 7,398 citations🎓Rochester Institute of TechnologyRochester · United States15 works · 5,233 citations

Also on the byline: Future House, Inc..

🧭 Fields, topics and concepts

Cite

Cite as Mayk Caldas Ramos, Christopher J. Collison and Andrew D. White. 2024. A Review of Large Language Models and Autonomous Agents in Chemistry. arXiv:2407.01603.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2407.01603 · arXiv 2407.01603

@misc{ramos2024review,
  title = {{A Review of Large Language Models and Autonomous Agents in Chemistry}},
  author = {Mayk Caldas Ramos, Christopher J. Collison and Andrew D. White},
  year = {2024},
  doi = {10.48550/arxiv.2407.01603},
  eprint = {2407.01603},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.01603}
}

References

577 references, 96 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Peter Willett. Chemoinformatics: a history. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(1):46–56, 2011. find
2. Edward J. Griffen, Alexander G. Dossetter, and Andrew G. Leach. Chemists: AI Is Here; Unite To Get the Benefits. Journal of Medicinal Chemistry, 63(16):8695–8704, August 2020. ISSN 0022-2623. doi:10.1021/acs.jmedchem.0c00163. URL https://doi.org/10.1021/acs.jmedchem.0c00163. Publisher: American Chem… — [doi:10.1021/acs.jmedchem.0c00163](https://doi.org/10.1021/acs.jmedchem.0c00163) ↗
3. Zachary J. Baum, Xiang Yu, Philippe Y. Ayala, Yanan Zhao, Steven P. Watkins, and Qiongqiong Zhou. Artificial Intelligence in Chemistry: Current Trends and Future Directions. Journal of Chemical Information and Modeling, 61(7):3197–3212, July 2021. ISSN 1549-9596. doi:10.1021/acs.jcim.1c00619. URL ht… — [doi:10.1021/acs.jcim.1c00619](https://doi.org/10.1021/acs.jcim.1c00619) find
4. Lucas B. Ayres, Federico J.V. Gomez, Jeb R. Linton, Maria F. Silva, and Carlos D. Garcia. Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Analytica Chimica Acta, 1161:338403, May 2021. ISSN 00032670. doi:10.1016/j.aca.2021.338403. URL https://linkinghub.e… — [doi:10.1016/j.aca.2021.338403](https://doi.org/10.1016/j.aca.2021.338403) ↗
5. Xin Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, and Shengyong Yang. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chemical Reviews, 119(18):10520–10594, September 2019. ISSN 0009-2665. doi:10.1021/acs.chemrev.8b00728. URL https://doi.org/10.1021/acs.chemrev.8b00728.… — [doi:10.1021/acs.chemrev.8b00728](https://doi.org/10.1021/acs.chemrev.8b00728) ↗
6. Adam C Mater and Michelle L Coote. Deep learning in chemistry. Journal of chemical information and modeling, 59(6):2545–2559, 2019. find
7. Yun-Fei Shi, Zheng-Xin Yang, Sicong Ma, Pei-Lin Kang, Cheng Shang, P Hu, and Zhi-Pan Liu. Machine learning for chemistry: basics and applications. Engineering, 2023. find
8. John A Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Muller, and Alexandre Tkatchenko. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chemical reviews, 121(16):9816–9872, 2021. find
9. David Kuntz and Angela K Wilson. Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory. Pure and Applied Chemistry, 94(8):1019–1054, 2022. find
10. Markus Meuwly. Machine learning for chemical reactions. Chemical Reviews, 121(16):10218–10239, 2021. find
11. Joshua Lederberg, Georgia L Sutherland, Bruce G Buchanan, Edward A Feigenbaum, Alexander V Robertson, Alan M Duffield, and Carl Djerassi. Applications of artificial intelligence for chemical inference. i. number of possible organic compounds. acyclic structures containing carbon, hydrogen, oxygen, a… find
12. Robert K Lindsay, Bruce G Buchanan, Edward A Feigenbaum, and Joshua Lederberg. Dendral: a case study of the first expert system for scientific hypothesis formation. Artificial intelligence, 61(2):209–261, 1993. find
13. B. G. Buchanan, D. H. Smith, W. C. White, R. J. Gritter, E. A. Feigenbaum, J. Lederberg, and Carl Djerassi. Applications of artificial intelligence for chemical inference. 22. Automatic rule formation in mass spectrometry by means of the meta-DENDRAL program. Journal of the American Chemical Society… — [doi:10.1021/ja00436a017](https://doi.org/10.1021/ja00436a017) find
14. Corwin Hansch, Peyton P Maloney, Toshio Fujita, and Robert M Muir. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, 194(4824):178–180, 1962. find
15. Corwin Hansch and Toshio Fujita. p-σ-π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8):1616–1626, 1964. find
16. Corwin Hansch and Albert Leo. Exploring QSAR.: Fundamentals and applications in chemistry and biology, volume 1. American Chemical Society, 1995. find
17. John G Topliss and Robert J Costello. Chance correlations in structure-activity studies using multiple regression analysis. Journal of Medicinal Chemistry, 15(10):1066–1068, 1972. find
18. John N. Weinstein, Kurt W. Kohn, Michael R. Grever, Vellarkad N. Viswanadhan, Lawrence V. Rubinstein, Anne P. Monks, Dominic A. Scudiero, Lester Welch, Antonis D. Koutsoukos, August J. Chiausa, and Kenneth D. Paull. Neural Computing in Cancer Drug Development: Predicting Mechanism of Action. Science… — [doi:10.1126/science.1411538](https://doi.org/10.1126/science.1411538) find
19. William W Van Osdol, Timothy G Myers, Kenneth D Paull, Kurt W Kohn, and John N Weinstein. Use of the kohonen self-organizing map to study the mechanisms of action of chemotherapeutic agents. JNCI: Journal of the National Cancer Institute, 86(24):1853–1859, 1994. find
20. Brian B Goldman and W Patrick Walters. Machine learning in computational chemistry. Annual Reports in Computational Chemistry, 2:127–140, 2006. find
21. DA Pereira and JA Williams. Origin and evolution of high throughput screening. British journal of pharmacology, 152(1):53–61, 2007. find
22. José L. Medina-Franco, Marc A. Giulianotti, Gregory S. Welmaker, and Richard A. Houghten. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discovery Today, 18(9):495–501, May 2013. ISSN 1359-6446. doi:10.1016/j.drudis.2013.01.008. URL https://www.sciencedirect.com/science… — [doi:10.1016/j.drudis.2013.01.008](https://doi.org/10.1016/j.drudis.2013.01.008) ↗
23. Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh. Machine learning for molecular and materials science. Nature, 559(7715):547–555, July 2018. ISSN 1476-4687. doi:10.1038/s41586-018-0337-2. URL https://www.nature.com/articles/s41586-018-0337-2. Publisher: Nature Pub… — [doi:10.1038/s41586-018-0337-2](https://doi.org/10.1038/s41586-018-0337-2) ↗
24. Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Physical Review Letters, 108(5): 058301, January 2012. doi:10.1103/PhysRevLett.108.058301. URL https://link.aps.org/doi/10.1103… — [doi:10.1103/physrevlett.108.058301](https://doi.org/10.1103/physrevlett.108.058301) ↗
25. Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. J. Cheminform., 9(1):48, September 2017. ISSN 1758-2946,1758-2946. doi:10.1186/s13321-017-0235-x. URL https://jcheminf.biomedcentral.com/articles/10. 1186/s13321-017-023… — [doi:10.1186/s13321-017-0235-x](https://doi.org/10.1186/s13321-017-0235-x) ↗
26. Marwin H S Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci., 4(1):120–131, January 2018. ISSN 2374-7943,2374-7951. doi:10.1021/acscentsci.7b00512. URL https://pubs.acs.org/doi/abs/10.1… — [doi:10.1021/acscentsci.7b00512](https://doi.org/10.1021/acscentsci.7b00512) ↗
27. Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Central Science, 4(1):120–131, January 2018. ISSN 2374-7943. doi:10.1021/acscentsci.7b00512. URL https://doi.org/10.1021/acscentsci.… — [doi:10.1021/acscentsci.7b00512](https://doi.org/10.1021/acscentsci.7b00512) ↗
28. Anvita Gupta, Alex T Muller, Berend J H Huisman, Jens A Fuchs, Petra Schneider, and Gisbert Schneider. Generative recurrent networks for de novo drug design. Mol. Inform., 37(1-2):1700111, January 2018. ISSN 1868-1751,1868-1743. doi:10.1002/minf.201700111. URL https://onlinelibrary.wiley.com/doi/abs… — [doi:10.1002/minf.201700111](https://doi.org/10.1002/minf.201700111) ↗
29. Pavel Karpov, Guillaume Godin, and Igor V Tetko. Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J. Cheminform., 12(1):17, March 2020. ISSN 1758-2946,1758-2946. doi:10.1186/s13321-020-00423-w. URL https://link.springer.com/articles/10.1186/s13321-020-00423-w. ↗
30. K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller. SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24):241722, March 2018. ISSN 0021-9606. doi:10.1063/1.5019779. URL https://doi.org/10.1063/1.5019779. — [doi:10.1063/1.5019779](https://doi.org/10.1063/1.5019779) ↗
31. Maya Hirohara, Yutaka Saito, Yuki Koda, Kengo Sato, and Yasubumi Sakakibara. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinformatics, 19(Suppl 19):526, December 2018. ISSN 1471-2105,1471-2105. doi:10.1186/s12859-018-2523-5. URL https:… — [doi:10.1186/s12859-018-2523-5](https://doi.org/10.1186/s12859-018-2523-5) find
32. Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci., 10(2):370–377, January 2019. ISSN 2041-6520,2041-6539. doi:10.1039/c8… — [doi:10.1039/c8sc04228d](https://doi.org/10.1039/c8sc04228d) find
33. Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Benchmarking graph neural networks. Journal of Machine Learning Research, 24(43):1–48, 2023. find
34. Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B Wiltschko. A gentle introduction to graph neural networks. Distill, 6(9):e33, 2021. find
35. Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18–42, 2017. find
36. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020.
37. Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In International conference on machine learning, pages 1263–1272. PMLR, 2017.
38. Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecu… — [doi:10.1021/acscentsci.7b00572](https://doi.org/10.1021/acscentsci.7b00572) find
39. Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy BR Hayter, Richard Vickers, Charles Roberts, Jian Tang, et al. Utilizing graph machine learning within drug discovery and development. Briefings in bioinformatics, 22(6):bbab159, 2021. find
40. Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth Holm, Shyue Ping Ong, and Chris Wolverton. Recent advances and applications of deep learning methods in materials science. npj Computati… — [doi:10.1038/s41524-022-00734-6](https://doi.org/10.1038/s41524-022-00734-6) find
41. Victor Fung, Jiaxin Zhang, Eric Juarez, and Bobby G Sumpter. Benchmarking graph neural networks for materials chemistry. Npj Comput. Mater., 7(1):1–8, June 2021. ISSN 2057-3960,2057-3960. doi:10.1038/s41524-021-00554-0. URL https://www.nature.com/articles/s41524-021-00554-0. — [doi:10.1038/s41524-021-00554-0](https://doi.org/10.1038/s41524-021-00554-0) ↗
42. Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, and Pascal Friederich. Graph neural networks for materials science and chemistry. Commun. Mater., 3(1):93, November 2022. ISSN 2662-4443,2662-4443. doi:… — [doi:10.1038/s43246-022-00315-6](https://doi.org/10.1038/s43246-022-00315-6) find
43. David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28(1):31–36, February 1988. ISSN 0095-2338. doi:10.1021/ci00057a005. URL https://doi.org/10.1021/ci00057a005. Publisher: Amer… — [doi:10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005) ↗
44. Seyone Chithrananda, Gabe Grand, and Bharath Ramsundar. ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv [cs.LG], October 2020. doi:10.48550/arXiv.2010.09885. URL http://arxiv.org/abs/2010.09885. ↗
45. Juncai Li and Xiaofei Jiang. Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2021, September 2021. ISSN 1530-8669. doi:10.1155/2021/7181815. — [doi:10.1155/2021/7181815](https://doi.org/10.1155/2021/7181815) find
46. Ye Wang, Honggang Zhao, Simone Sciabola, and Wenlu Wang. cMolGPT: A conditional generative Pre-Trained transformer for Target-Specific de novo molecular generation. Molecules, 28(11), May 2023. ISSN 1420-3049. doi:10.3390/molecules28114430. URL http://dx.doi.org/10.3390/molecules28114430. — [doi:10.3390/molecules28114430](https://doi.org/10.3390/molecules28114430) ↗
47. Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Augmenting large language models with chemistry tools. Nature Machine Intelligence, pages 1–11, May 2024. ISSN 2522-5839, 2522-5839. doi:10.1038/s42256-024-00832-8. URL https://www.nature.com/ article… — [doi:10.1038/s42256-024-00832-8](https://doi.org/10.1038/s42256-024-00832-8) ↗
48. Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature, 624(7992):570–578, December 2023. ISSN 0028-0836,1476-4687. doi:10.1038/s41586-023-06792-0. URL https://www.nature.com/articles/s41586-023-06792-0. ↗
49. Andrew D White. The future of chemistry is language. Nature Reviews Chemistry, 7(7):457–458, July 2023. ISSN 2397-3358. doi:10.1038/s41570-023-00502-0. URL https://www.nature.com/articles/ s41570-023-00502-0. — [doi:10.1038/s41570-023-00502-0](https://doi.org/10.1038/s41570-023-00502-0) ↗
50. Christopher J. Collison, Marc J. O’Donnell, and Jessica L. Alexander. Complexation between Rhodamine 101 and Single-Walled Carbon Nanotubes Indicative of Solvent-Nanotube Interaction Strength. The Journal of Physical Chemistry C, 112(39):15144–15150, October 2008. ISSN 1932-7447. doi:10.1021/jp80435… — [doi:10.1021/jp804359j](https://doi.org/10.1021/jp804359j) find
51. Tyler J. Wiegand, Juan S. Sandoval, Jeremy A. Cody, David W. McCamant, and Christopher J. Collison. Directional Exciton Diffusion, Measured by Subpicosecond Transient Absorption as an Explanation for Squaraine Solar Cell Performance. The Journal of Physical Chemistry C, 128(11):4616–4630, March 2024… — [doi:10.1021/acs.jpcc.3c06361](https://doi.org/10.1021/acs.jpcc.3c06361) find
52. Rashad Ahmadov, Shane S. Michtavy, and Marc D. Porosoff. Dual Functional Materials: At the Interface of Catalysis and Separations. Langmuir, 40(19):9833–9841, May 2024. ISSN 0743-7463. doi:10.1021/acs.langmuir.3c03888. URL https://doi.org/10.1021/acs.langmuir.3c03888. Publisher: American Chemical So… — [doi:10.1021/acs.langmuir.3c03888](https://doi.org/10.1021/acs.langmuir.3c03888) ↗
53. Thomas Fischer, Silvia Gazzola, and Rainer Riedl. Approaching Target Selectivity by De Novo Drug Design. Expert Opinion on Drug Discovery, 14(8):791–803, August 2019. ISSN 1746-0441. doi:10.1080/17460441.2019.1615435. URL https://doi.org/10.1080/17460441.2019.1615435. Publisher: Taylor & Francis _ep… — [doi:10.1080/17460441.2019.1615435](https://doi.org/10.1080/17460441.2019.1615435) ↗
54. Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, and Xue-Feng Yu. Data-Driven Materials Innovation and Applications. Advanced Materials, 34(36):2104113, 2022. ISSN 1521-4095. doi:10.1002/adma.202104113. URL https://onlinelibrar… — [doi:10.1002/adma.202104113](https://doi.org/10.1002/adma.202104113) find
55. Bhuvanesh Sridharan, Manan Goel, and U. Deva Priyakumar. Modern machine learning for tackling inverse problems in chemistry: molecular design to realization. Chemical Communications, 58(35):5316–5331, April 2022. ISSN 1364-548X. doi:10.1039/D1CC07035E. URL https://pubs.rsc.org/en/content/ articlelan… — [doi:10.1039/d1cc07035e](https://doi.org/10.1039/d1cc07035e) ↗
56. Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. MoleculeNet: a benchmark for molecular machine learning. Chemical Science, 9(2): 513–530, 2018. ISSN 2041-6520, 2041-6539. doi:10.1039/C7SC02664A. URL http://xlink.rsc.org/… — [doi:10.1039/c7sc02664a](https://doi.org/10.1039/c7sc02664a) ↗
57. Guillermo Restrepo. Chemical space: limits, evolution and modelling of an object bigger than our universal library. Digital Discovery, 1(5):568–585, October 2022. ISSN 2635-098X. doi:10.1039/D2DD00030J. URL https://pubs.rsc.org/en/content/articlelanding/2022/dd/d2dd00030j. Publisher: RSC. — [doi:10.1039/d2dd00030j](https://doi.org/10.1039/d2dd00030j) ↗
58. Peter Kirkpatrick and Clare Ellis. Chemical space. Nature, 432(7019):823–824, 2004. find
59. Asher Mullard et al. The drug-maker’s guide to the galaxy. Nature, 549(7673):445–447, 2017. find
60. Eugenio J Llanos, Wilmer Leal, Duc H Luu, Jürgen Jost, Peter F Stadler, and Guillermo Restrepo. Exploration of the chemical space and its three historical regimes. Proceedings of the National Academy of Sciences, 116 (26):12660–12665, 2019. find
61. Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, and Jakoah Brgoch. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science. Journal of the American Chemical Society, 145(40):21699–21716, October 2023. ISSN 0002-7863. doi:10.1021/jacs.3c04783… — [doi:10.1021/jacs.3c04783](https://doi.org/10.1021/jacs.3c04783) find
62. Piotr S Gromski, Alon B Henson, Jarosław M Granda, and Leroy Cronin. How to explore chemical space using algorithms and automation. Nature Reviews Chemistry, 3(2):119–128, 2019. find
63. Sebastian Steiner, Jakob Wolf, Stefan Glatzel, Anna Andreou, Jarosław M Granda, Graham Keenan, Trevor Hinkley, Gerardo Aragon-Camarasa, Philip J Kitson, Davide Angelone, et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science, 363(6423):eaav2211, 2019. find
64. Benjamin Burger, Phillip M Maffettone, Vladimir V Gusev, Catherine M Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M Alston, Buyi Li, Rob Clowes, et al. A mobile robotic chemist. Nature, 583(7815):237–241, 2020. find
65. Benjamin P MacLeod, Fraser GL Parlane, Thomas D Morrissey, Florian Häse, Loïc M Roch, Kevan E Dettelbach, Raphaell Moreira, Lars PE Yunker, Michael B Rooney, Joseph R Deeth, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Science Advances, 6(20):eaaz8867, 2020. find
66. Qiang Zhang, Keyang Ding, Tianwen Lyv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Mengyao Zhang, Jinlu Zhang, Jiyu Cui, Renjun Xu, Hongyang Chen, Xiaohui Fan, Huabin Xing, and Huajun Chen. Scientific large language models… find
67. David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed. de rumelhart and j. mcclelland. vol. 1. 1986. Biometrika, 71:599–607, 1986. find
68. Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. find
69. Antônio H. Ribeiro, Koen Tiels, Luis A. Aguirre, and Thomas B. Schön. Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness, March 2020. URL http://arxiv.org/ abs/1906.08482. arXiv:1906.08482 [cs, math, stat]. — [arXiv:1906.08482](https://arxiv.org/abs/1906.08482) ↗
70. Dr Barak Or. The Exploding and Vanishing Gradients Problem in Time Series, December 2023. URL https://medium.com/metaor-artificial-intelligence/ the-exploding-and-vanishing-gradients-problem-in-time-series-6b87d558d22. ↗
71. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. arXiv [cs.CL], June 2017. URL http://arxiv.org/abs/ 1706.03762. ↗
72. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv [cs.LG], December 2023. URL http://arxiv.org/abs/2312.00752. ↗
73. Samy Jelassi, David Brandfonbrener, Sham M Kakade, and Eran Malach. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG], February 2024. URL http://arxiv.org/abs/2402. 01032. ↗
74. Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al. Rwkv: Reinventing rnns for the transformer era. arXiv preprint arXiv:2305.13048, 2023. find
75. Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter. xLSTM: Extended Long Short-Term Memory, May 2024. URL http://arxiv.org/abs/2405.04517. arXiv:2405.04517 [cs, stat]. ↗
76. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. Large language models: A survey. arXiv [cs.CL], February 2024. URL http://arxiv.org/ abs/2402.06196. ↗
77. The Annotated Transformer, 2022. URL https://nlp.seas.harvard.edu/annotated-transformer/. ↗
78. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural Machine Translation by Jointly Learning to Align and Translate, May 2016. URL http://arxiv.org/abs/1409.0473. arXiv:1409.0473 [cs, stat]. ↗
79. Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, and Wenhu Chen. Long-context LLMs struggle with long in-context learning. arXiv [cs.CL], April 2024. URL http://arxiv.org/abs/2404.02060. ↗
80. Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, and Sercan Ö Arik. Chain of agents: Large language models collaborating on long-context tasks. arXiv [cs.CL], June 2024. URL http://arxiv.org/ abs/2406.02818. ↗
81. Taku Kudo. Subword regularization: Improving neural network translation models with multiple subword candidates. arXiv [cs.CL], April 2018. URL http://arxiv.org/abs/1804.10959. ↗
82. Taku Kudo and John Richardson. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv [cs.CL], August 2018. URL http://arxiv.org/abs/1808. 06226. ↗
83. Xinying Song, Alex Salcianu, Yang Song, Dave Dopson, and Denny Zhou. Fast WordPiece tokenization. arXiv [cs.CL], December 2020. URL http://arxiv.org/abs/2012.15524. ↗
84. Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, and Iryna Gurevych. How good is your tokenizer? on the monolingual performance of multilingual language models. arXiv [cs.CL], December 2020. URL http://arxiv.org/abs/2012.15613. ↗
85. Martin Berglund and Brink van der Merwe. Formalizing BPE tokenization. arXiv [cs.FL], September 2023. URL http://arxiv.org/abs/2309.08715. — [arXiv:2309.08715](https://arxiv.org/abs/2309.08715) ↗
86. Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. Convolutional sequence to sequence learning. arXiv [cs.CL], pages 1243–1252, May 2017. URL https://proceedings.mlr.press/ v70/gehring17a.html. ↗
87. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv [cs.CL], October 2018. doi:10.48550/arXiv.1810.04805. URL http://arxiv.org/abs/1810.04805. ↗
88. Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. find
89. Noam Shazeer. GLU Variants Improve Transformer, February 2020. URL http://arxiv.org/abs/2002. 05202. arXiv:2002.05202 [cs, stat]. ↗
90. Dan Hendrycks and Kevin Gimpel. Gaussian Error Linear Units (GELUs), June 2023. URL http://arxiv.org/abs/1606.08415. arXiv:1606.08415 [cs]. ↗
91. Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Jiawei Han, and Lianhui Qin. Structured chemistry reasoning with large language models. arXiv [cs.CL], November 2023. URL http://arxiv.org/abs/2311. 09656. ↗
92. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008–3021, 2020. find
93. Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M Ziegler, Jeffrey Wu, Clemens Winter, Ch… — Language Models are Few-Shot Learners
94. Federico Errica, Giuseppe Siracusano, Davide Sanvito, and Roberto Bifulco. What did I do wrong? quantifying LLMs’ sensitivity and consistency to prompt engineering. arXiv [cs.LG], June 2024. URL http://arxiv.org/abs/2406.12334. ↗
95. Tianhao Shen, Renren Jin, Yufei Huang, Chuang Liu, Weilong Dong, Zishan Guo, Xinwei Wu, Yan Liu, and Deyi Xiong. Large language model alignment: A survey. arXiv [cs.CL], September 2023. URL http://arxiv.org/abs/2309.15025. ↗
96. Changho Lee, Janghoon Han, Seonghyeon Ye, Stanley Jungkyu Choi, Honglak Lee, and Kyunghoon Bae. Instruction matters, a simple yet effective task selection approach in instruction tuning for specific tasks. arXiv [cs.CL], April 2024. URL http://arxiv.org/abs/2404.16418. — [arXiv:2404.16418](https://arxiv.org/abs/2404.16418) ↗
97. John Hewitt, Nelson F Liu, Percy Liang, and Christopher D Manning. Instruction following without instruction tuning. arXiv [cs.CL], September 2024. URL http://arxiv.org/abs/2409.14254. ↗
98. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang. Instruction tuning for large language models: A survey. arXiv [cs.CL], August 2023. URL http://arxiv.org/abs/2308.10792. ↗
99. Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel. RL 2 : Fast reinforcement learning via slow reinforcement learning. arXiv [cs.AI], November 2016. URL http://arxiv.org/abs/ 1611.02779. ↗
100. Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv [cs.CL], September 2019. URL http://arxiv.org/abs/1909.08593. ↗
101. Eyal Mazuz, Guy Shtar, Bracha Shapira, and Lior Rokach. Molecule generation using transformers and policy gradient reinforcement learning. Sci. Rep., 13(1):8799, May 2023. ISSN 2045-2322. doi:10.1038/s41598-023-35648-w. URL http://dx.doi.org/10.1038/s41598-023-35648-w. — [doi:10.1038/s41598-023-35648-w](https://doi.org/10.1038/s41598-023-35648-w) ↗
102. Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, D J Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, and Volodymyr Mnih. In-context reinforcement learning with algorithm distillation. arXiv [cs.LG], Octo… — [arXiv:2210.14215](https://arxiv.org/abs/2210.14215) find
103. Aligning language models to follow instructions. https://openai.com/index/instruction-following, 2022. URL https://openai.com/index/instruction-following. Accessed: 2024-5-1. ↗
104. Sungdong Kim, Sanghwan Bae, Jamin Shin, Soyoung Kang, Donghyun Kwak, Kang Min Yoo, and Minjoon Seo. Aligning large language models through synthetic feedback. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.13735. ↗
105. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv [cs.LG], July 2017. URL http://arxiv.org/abs/1707.06347. ↗
106. Junzi Zhang, Jongho Kim, Brendan O’Donoghue, and Stephen Boyd. Sample efficient reinforcement learning with REINFORCE. arXiv [cs.LG], October 2020. URL http://arxiv.org/abs/2010.11364. ↗
107. Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. arXiv [cs.AI], March 2023. URL http://arxiv.org/abs/2303.11366. ↗
108. Afra Feyza Akyurek, Ekin Akyurek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, and Niket Tandon. RL4F: Generating natural language feedback with reinforcement learning for repairing model outputs. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.08844. ↗
109. Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu, Guolong Liu, Gaoqi Liang, Junhua Zhao, and Yun Li. Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods. arXiv [cs.LG], March 2024. URL http://arxiv.org/abs/2404.00282. ↗
110. Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. arXiv [cs.LG], May 2023. URL http://arxiv.org/abs/2305.18290. ↗
111. Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. RRHF: Rank responses to align language models with human feedback without tears. arXiv [cs.CL], April 2023. URL http://arxiv.org/abs/2304.05302. ↗
112. Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, and Houfeng Wang. Preference ranking optimization for human alignment. arXiv [cs.CL], June 2023. URL http://arxiv.org/abs/2306. 17492. ↗
113. Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, and Yi Wu. Is DPO superior to PPO for LLM alignment? a comprehensive study. arXiv [cs.CL], April 2024. URL http://arxiv.org/abs/2404.10719. — [arXiv:2404.10719](https://arxiv.org/abs/2404.10719) ↗
114. Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro Ramírez, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen Ahmad, Vincent Moens, et al. Acegen: Reinforcement learning of generative chemical agents for drug discovery. arXiv preprint arXiv:2405.04657, 2024. — [arXiv:2405.04657](https://arxiv.org/abs/2405.04657) find
115. Thomas Hayes, Roshan Rao, Halil Akin, Nicholas James Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Quy Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul Santiago Molina, Neil Thomas, Yousuf Khan, Chetan Mishra, Carolyn Kim, Liam J. B… find
116. Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan. BioT5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empi… find
117. Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, and Rui Yan. BioT5+: Towards generalized biological understanding with IUPAC integration and multi-task tuning. arXiv [q-bio.QM], February 2024. URL http://arxiv.org/abs/2402.17810. ↗
118. Juncai Li and Xiaofei Jiang. Mol-BERT: An effective molecular representation with BERT for molecular property prediction. Proc. Int. Wirel. Commun. Mob. Comput. Conf., 2021, September 2021. ISSN 1530-8669, 1530-8669. doi:10.1155/2021/7181815. URL https://www.hindawi.com/journals/wcmc/2021/7181815/. — [doi:10.1155/2021/7181815](https://doi.org/10.1155/2021/7181815) ↗
119. Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, and Yong Liu. Can large language models empower molecular property prediction? July 2023. URL http://arxiv.org/abs/2307.07443. — [arXiv:2307.07443](https://arxiv.org/abs/2307.07443) ↗
120. Thanh-Hoang Nguyen-Vo, Quang H. Trinh, Loc Nguyen, Trang T. T. Do, Matthew Chin Heng Chua, and Binh P. Nguyen. Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 62(21): 5050–505… — [doi:10.1021/acs.jcim.1c00584](https://doi.org/10.1021/acs.jcim.1c00584) find
121. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. PubMedQA: A dataset for biomedical research question answering. arXiv [cs.CL], September 2019. URL http://arxiv.org/abs/ 1909.06146. ↗
122. Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. ChemBERTa-2: Towards Chemical Foundation Models, September 2022. URL http://arxiv.org/abs/2209.01712. arXiv:2209.01712 [cs, q-bio]. — [arXiv:2209.01712](https://arxiv.org/abs/2209.01712) ↗
123. Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv [cs.CL], November 2022. URL http://arxiv.org/abs/2211.09085. ↗
124. Wilson L. Taylor. “Cloze Procedure”: A New Tool for Measuring Readability. Journalism Quarterly, 30(4): 415–433, September 1953. ISSN 0022-5533. doi:10.1177/107769905303000401. URL https://doi.org/ 10.1177/107769905303000401. Publisher: SAGE Publications. — [doi:10.1177/107769905303000401](https://doi.org/10.1177/107769905303000401) find
125. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A Robustly Optimized BERT Pretraining Approach, July 2019. URL http://arxiv.org/abs/1907.11692. arXiv:1907.11692 [cs]. ↗
126. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding with unsupervised learning. 2018. find
127. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension, October 2019. URL http://arxiv.org/abs/1910.13461. arXiv:1… ↗
128. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv [cs.LG], October 2019. URL http://arxiv.org/abs/1910.10683. ↗
129. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan… find
130. Bowen Tan, Yun Zhu, Lijuan Liu, Eric Xing, Zhiting Hu, and Jindong Chen. Cappy: Outperforming and boosting large multi-task LMs with a small scorer. arXiv [cs.LG], November 2023. doi:10.48550/arXiv.2311.06720. URL https://proceedings.neurips.cc/paper_files/paper/2023/ file/b860c0c546f4a3a786f9c94682… — [doi:10.48550/arxiv.2311.06720](https://doi.org/10.48550/arxiv.2311.06720) ↗
131. Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, and Nicolo Fusi. Tag-LLM: Repurposing general-purpose LLMs for specialized domains. arXiv [cs.LG], February 2024. URL http://arxiv.org/abs/2402.05140. — [arXiv:2402.05140](https://arxiv.org/abs/2402.05140) ↗
132. Guijin Son, Sangwon Baek, Sangdae Nam, Ilgyun Jeong, and Seungone Kim. Multi-task inference: Can large language models follow multiple instructions at once? arXiv [cs.CL], February 2024. URL http://arxiv.org/abs/2402.11597. ↗
133. Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Yu Han, and Hao Wang. Mixture-of-LoRAs: An efficient multitask tuning for large language models. arXiv [cs.CL], March 2024. URL http://arxiv.org/abs/2403.03432. — [arXiv:2403.03432](https://arxiv.org/abs/2403.03432) ↗
134. Fuyu-8B: A multimodal architecture for AI agents. https://www.adept.ai/blog/fuyu-8b, 2023. URL https://www.adept.ai/blog/fuyu-8b. Accessed: 2023-11-8. ↗
135. Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. NExT-GPT: Any-to-any multimodal LLM. arXiv [cs.AI], September 2023. URL http://arxiv.org/abs/2309.05519. ↗
136. Debjyoti Bhattacharya, Harrison Cassady, Michael Hickner, and Wesley Reinhart. Large language models as molecular design engines. ChemRxiv, May 2024. doi:10.26434/chemrxiv-2024-n0l8q. URL https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/ 664b795e418a5379b0d12460/original/lar… — [doi:10.26434/chemrxiv-2024-n0l8q](https://doi.org/10.26434/chemrxiv-2024-n0l8q) ↗
137. Mantas Vaškevičius, Jurgita Kapočiūtė-Dzikienė, and Liudas Šlepikas. Generative LLMs in organic chemistry: Transforming esterification reactions into natural language procedures. Appl. Sci. (Basel), 13(24):13140, December 2023. ISSN 2076-3417,2076-3417. doi:10.3390/app132413140. URL https://www… — [doi:10.3390/app132413140](https://doi.org/10.3390/app132413140) find
138. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. arXiv [eess.AS], December 2022. URL http://arxiv.org/abs/2212.04356. ↗
139. Jannis Born and Matteo Manica. Regression Transformer enables concurrent sequence regression and generation for molecular language modelling. Nat. Mach. Intell., 5(4):432–444, April 2023. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-023-00639-z. URL https://www.nature.com/articles/s42256-023-00639-z… — [doi:10.1038/s42256-023-00639-z](https://doi.org/10.1038/s42256-023-00639-z) ↗
140. Jiashun Mao, Jianmin Wang, Kwang-Hwi Cho, and Kyoung Tai No. iupacGPT: IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation. ChemRxiv, pages 1– 13, May 2023. doi:10.26434/chemrxiv-2023-5kjvh. URL https://chemrxiv.org/engage/chemrxiv/ public-dashboard. — [doi:10.26434/chemrxiv-2023-5kjvh](https://doi.org/10.26434/chemrxiv-2023-5kjvh) ↗
141. Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, and Brandon M. Wood. From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction, May 2024. URL http://arxiv.org/abs/2310.16802. arXiv:2310.16802 [cs]. — [arXiv:2310.16802](https://arxiv.org/abs/2310.16802) ↗
142. Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit. Leveraging large language models for predictive chemistry. Nat. Mach. Intell., 6(2):161–169, February 2024. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-023-00788-1. URL https://www.nature.com/articles/s42256-023-00788… ↗
143. Ryan Jacobs, Maciej P Polak, Lane E Schultz, Hamed Mahdavi, Vasant Honavar, and Dane Morgan. Regression with large language models for materials and molecular property prediction. arXiv [cond-mat.mtrl-sci], September 2024. URL http://arxiv.org/abs/2409.06080. — [arXiv:2409.06080](https://arxiv.org/abs/2409.06080) ↗
144. Yu-Chen Lo, Stefano E Rensi, Wen Torng, and Russ B Altman. Machine learning in chemoinformatics and drug discovery. Drug discovery today, 23(8):1538–1546, 2018. find
145. Laurianne David, Amol Thakkar, Rocío Mercado, and Ola Engkvist. Molecular representations in AI-driven drug discovery: a review and practical guide. Journal of Cheminformatics, 12(1):56, September 2020. ISSN 1758-2946. doi:10.1186/s13321-020-00460-5. URL https://doi.org/10.1186/s13321-020-00460-5. — [doi:10.1186/s13321-020-00460-5](https://doi.org/10.1186/s13321-020-00460-5) ↗
146. Kenneth Atz, Francesca Grisoni, and Gisbert Schneider. Geometric deep learning on molecular representations. arXiv [physics.chem-ph], July 2021. URL https://www.nature.com/articles/s42256-021-00418-8. ↗
147. W Patrick Walters and Regina Barzilay. Applications of deep learning in molecule generation and molecular property prediction. Acc. Chem. Res., 54(2):263–270, January 2021. ISSN 0001-4842,1520-4898. doi:10.1021/acs.accounts.0c00699. URL https://pubs.acs.org/doi/abs/10.1021/acs.accounts. 0c00699. ↗
148. Akshaya Karthikeyan and U Deva Priyakumar. Artificial intelligence: machine learning for chemical sciences. J. Chem. Sci. (Bangalore), 134(1):2, 2022. ISSN 0974-3626,0973-7103. doi:10.1007/s12039-021-01995-2. URL https://link.springer.com/article/10.1007/s12039-021-01995-2. — [doi:10.1007/s12039-021-01995-2](https://doi.org/10.1007/s12039-021-01995-2) ↗
149. Zhen Li, Mingjian Jiang, Shuang Wang, and Shugang Zhang. Deep learning methods for molecular representation and property prediction. Drug Discov. Today, 27(12):103373, December 2022. ISSN 1359-6446,1878-5832. doi:10.1016/j.drudis.2022.103373. URL http://dx.doi.org/10.1016/j.drudis.2022.103373. — [doi:10.1016/j.drudis.2022.103373](https://doi.org/10.1016/j.drudis.2022.103373) ↗
150. Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. Graph networks as a universal machine learning framework for molecules and crystals. Chemistry of Materials, 31(9):3564–3572, 2019. find
151. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118–22133, 2020. find
152. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley. Molecular graph convolutions: moving beyond fingerprints. Journal of computer-aided molecular design, 30:595–608, 2016. find
153. Yeji Wang, Shuo Wu, Yanwen Duan, and Yong Huang. A point cloud-based deep learning strategy for protein– ligand binding affinity prediction. Briefings in Bioinformatics, 23(1):bbab474, 2022. find
154. Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv preprint arXiv:1802.08219, 2018. — [arXiv:1802.08219](https://arxiv.org/abs/1802.08219) find
155. Liguo Wang, Lu Zhao, Xian Liu, Jianjie Fu, and Aiqian Zhang. Seppcnet: deeping learning on a 3d surface electrostatic potential point cloud for enhanced toxicity classification and its application to suspected environmental estrogens. Environmental Science & Technology, 55(14):9958–9967, 2021. find
156. Soroush Ahmadi, Mohammad Amin Ghanavati, and Sohrab Rohani. Machine learning-guided prediction of cocrystals using point cloud-based molecular representation. Chemistry of Materials, 2024. find
157. Sukriti Singh and Raghavan B. Sunoj. Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges. Accounts of Chemical Research, 56(3):402–412, February 2023. ISSN 0001-4842. doi:10.1021/acs.accounts.2c00801. URL https://doi.org/10.1021/acs.accounts.2c00801. Publisher: American Chemi… — [doi:10.1021/acs.accounts.2c00801](https://doi.org/10.1021/acs.accounts.2c00801) ↗
158. Andres M Bran and Philippe Schwaller. Transformers and large language models for chemistry and drug discovery. arXiv [cs.LG], October 2023. URL http://arxiv.org/abs/2310.06083. — [arXiv:2310.06083](https://arxiv.org/abs/2310.06083) ↗
159. Shilpa Shilpa, Gargee Kashyap, and Raghavan B. Sunoj. Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions. The Journal of Physical Chemistry A, 127(40):8253– 8271, October 2023. ISSN 1089-5639. doi:10.1021/acs.jpca.3c04779. URL https://doi.org/10.1… — [doi:10.1021/acs.jpca.3c04779](https://doi.org/10.1021/acs.jpca.3c04779) find
160. Daniel S Wigh, Jonathan M Goodman, and Alexei A Lapkin. A review of molecular representation in the age of machine learning. Wiley Interdiscip. Rev. Comput. Mol. Sci., 12(5), September 2022. ISSN 1759-0876,1759-0884. doi:10.1002/wcms.1603. URL https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1… — [doi:10.1002/wcms.1603](https://doi.org/10.1002/wcms.1603) ↗
161. Noel O’Boyle and Andrew Dalke. DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures, September 2018. URL https://chemrxiv.org/engage/chemrxiv/ article-details/60c73ed6567dfe7e5fec388d. ↗
162. Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alán Aspuru-Guzik. Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation. Machine Learning: Science and Technology, 1(4):045024, December 2020. ISSN 2632-2153. doi:10.1088/2632-2153/aba947. URL… — [doi:10.1088/2632-2153/aba947](https://doi.org/10.1088/2632-2153/aba947) find
163. Stephen R. Heller, Alan McNaught, Igor Pletnev, Stephen Stein, and Dmitrii Tchekhovskoi. InChI, the IUPAC International Chemical Identifier. Journal of Cheminformatics, 7(1):23, May 2015. ISSN 1758-2946. doi:10.1186/s13321-015-0068-4. URL https://doi.org/10.1186/s13321-015-0068-4. — [doi:10.1186/s13321-015-0068-4](https://doi.org/10.1186/s13321-015-0068-4) ↗
164. Manajit Das, Ankit Ghosh, and Raghavan B Sunoj. Advances in machine learning with chemical language models in molecular property and reaction outcome predictions. J. Comput. Chem., February 2024. ISSN 0192-8651, 1096-987X. doi:10.1002/jcc.27315. URL http://dx.doi.org/10.1002/jcc.27315. — [doi:10.1002/jcc.27315](https://doi.org/10.1002/jcc.27315) ↗
165. John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman. Zinc: a free tool to discover chemistry for biology. Journal of chemical information and modeling, 52(7):1757–1768, 2012. find
166. Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al. Pubchem substance and compound databases. Nucleic acids research, 44(D1):D1202–D1213, 2016. find
167. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.
168. Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, and John P Overington. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 40(Database issue):D1100–7, Januar… — [doi:10.1093/nar/gkr777](https://doi.org/10.1093/nar/gkr777) find
169. Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, David Graham, Fangzhou Hu… find
170. Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, and Huajun Chen. Mol-instructions: A large-scale biomolecular instruction dataset for large language models. arXiv [q-bio.QM], June 2023. URL http://arxiv.org/abs/2306.08018. ↗
171. Jin-Mao Wei, Xiao-Jie Yuan, Qing-Hua Hu, and Shu-Qin Wang. A novel measure for evaluating classifiers. Expert Syst. Appl., 37(5):3799–3809, May 2010. ISSN 0957-4174,1873-6793. doi:10.1016/j.eswa.2009.11.040. URL http://dx.doi.org/10.1016/j.eswa.2009.11.040. — [doi:10.1016/j.eswa.2009.11.040](https://doi.org/10.1016/j.eswa.2009.11.040) ↗
172. Martin Krallinger, Obdulia Rabal, Florian Leitner, Miguel Vazquez, David Salgado, Zhiyong Lu, Robert Leaman, Yanan Lu, Donghong Ji, Daniel M Lowe, Roger A Sayle, Riza Theresa Batista-Navarro, Rafal Rak, Torsten Huber, Tim Rocktäschel, Sérgio Matos, David Campos, Buzhou Tang, Hua Xu, Tsendsuren Munkh… — [doi:10.1186/1758-2946-7-s1-s2](https://doi.org/10.1186/1758-2946-7-s1-s2) find
173. Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford), 2016:baw068, May 2016. ISSN 1758-0463… — [doi:10.1093/database/baw068](https://doi.org/10.1093/database/baw068) find
174. Rezarta Islamaj Dogan, Sun Kim, Andrew Chatr-Aryamontri, Chih-Hsuan Wei, Donald C Comeau, Rui Antunes, Sérgio Matos, Qingyu Chen, Aparna Elangovan, Nagesh C Panyam, Karin Verspoor, Hongfang Liu, Yanshan Wang, Zhuang Liu, Berna Altinel, Zehra Melce Hüsünbeyi, Arzucan Özgür, Aris Fergadis, Chen-Kai Wa… — [doi:10.1093/database/bay147](https://doi.org/10.1093/database/bay147) find
175. M Ashburner, C A Ball, J A Blake, D Botstein, H Butler, J M Cherry, A P Davis, K Dolinski, S S Dwight, J T Eppig, M A Harris, D P Hill, L Issel-Tarver, A Kasarskis, S Lewis, J C Matese, J E Richardson, M Ringwald, G M Rubin, and G Sherlock. Gene ontology: tool for the unification of biology. the gen… — [doi:10.1038/75556](https://doi.org/10.1038/75556) find
176. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In International Conference on Learning Representations, October 2020. URL https://openreview.net/pdf?id=d7KBjmI3GmQ. ↗
177. Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res., 49(D1):D1388–D1395, January 2021. I… — [doi:10.1093/nar/gkaa971](https://doi.org/10.1093/nar/gkaa971) find
178. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. MedMCQA: A large-scale multi-subject multi-choice dataset for medical domain question answering. In Conference on Health, Inference, and Learning, pages 248–260. PMLR, April 2022. URL — MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering https://proceedings.mlr.press/v174/pal22a.html. ↗
179. Jieyu Lu and Yingkai Zhang. Unified Deep Learning Model for Multitask Reaction Predictions with Explanation. J. Chem. Inf. Model., 62(6):1376–1387, March 2022. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.1c01467. URL https://doi.org/10.1021/acs.jcim.1c01467. — [doi:10.1021/acs.jcim.1c01467](https://doi.org/10.1021/acs.jcim.1c01467) ↗
180. UniProt Consortium. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res., 51(D1): D523–D531, January 2023. ISSN 0305-1048,1362-4962. doi:10.1093/nar/gkac1052. URL https://academic.oup.com/nar/article-pdf/51/D1/D523/48441158/gkac1052.pdf. — [doi:10.1093/nar/gkac1052](https://doi.org/10.1093/nar/gkac1052) ↗
181. Marwin HS Segler, Mike Preuss, and Mark P Waller. Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604–610, 2018. find
182. awesome-chemistry-datasets/code_of_conduct.md at main · kjappelbaum/awesome-chemistry-datasets, 2023. URL https://github.com/kjappelbaum/awesome-chemistry-datasets/blob/main/code_ of_conduct.md. ↗
183. Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Benedict Emoekabu, Aswanth Krishnan, Mara Wilhelmi, Macjonathan Okereke, Juliane Eberhardt, Amir Mohammad Elahi, Maximilian Greiner, Caroline T Holick, Tanya Gupta, Mehrdad Asgari, Christina Glaubitz, Lea C Klepsch, Yannik Koster, Jakob Meyer, Santia… — [arXiv:2404.01475](https://arxiv.org/abs/2404.01475) find
184. David E Gloriam. Bigger is better in virtual drug screens. Nature, 566(7743):193–194, February 2019. ISSN 0028-0836,1476-4687. doi:10.1038/d41586-019-00145-6. URL http://dx.doi.org/10.1038/ d41586-019-00145-6. — [doi:10.1038/d41586-019-00145-6](https://doi.org/10.1038/d41586-019-00145-6) find
185. Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology, 3(1):015022, January 2022. ISSN 2632-2153. doi:10.1088/2632-2153/ac3ffb. URL https://dx.doi.org/10.1088/2632-2153/ac3ff… — [doi:10.1088/2632-2153/ac3ffb](https://doi.org/10.1088/2632-2153/ac3ffb) ↗
186. Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Animashree Anandkumar. Multi-modal molecule structure–text model for text-based retrieval and editing. Nature Machine Intelligence, 5(12):1447–1457, December 2023. ISSN 2522-5839. doi:10.1038/s4… find
187. Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, Alán Aspuru-Guzik, and Alex Zhavoronkov. Nach0: Multimodal natural and chemical languages foundation model. arXiv [cs.CL], November 2023. URL ht… — [arXiv:2311.12410](https://arxiv.org/abs/2311.12410) find
188. Pat Walters. We need better benchmarks for machine learning in drug discovery, 2023. URL http://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine. html. ↗
189. Cheng Fang, Ye Wang, Richard Grater, Sudarshan Kapadnis, Cheryl Black, Patrick Trapa, and Simone Sciabola. Prospective validation of machine learning algorithms for absorption, distribution, metabolism, and excretion prediction: An industrial perspective. J. Chem. Inf. Model., 63(11):3263–3274, June… — [doi:10.1021/acs.jcim.3c00160](https://doi.org/10.1021/acs.jcim.3c00160) find
190. Therapeutics data commons. https://tdcommons.ai/. Accessed: 2024-6-13. ↗
191. Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. arXiv [cs.LG], February 2021. URL http://arxiv.org/abs/2102.09548. ↗
192. Alejandro Velez-Arce, Kexin Huang, Michelle M Li, Xiang Lin, Wenhao Gao, Tianfan Fu, Manolis Kellis, Bradley L Pentelute, and Marinka Zitnik. TDC-2: Multimodal foundation for therapeutic science. bioRxiv, page 2024.06.12.598655, June 2024. doi:10.1101/2024.06.12.598655. URL https://www.biorxiv.org/… — [doi:10.1101/2024.06.12.598655](https://doi.org/10.1101/2024.06.12.598655) ↗
193. Alex Rich and Ben Birnbaum. Building adme benchmark datasets that drive impact, 2023. URL https://www.inductive.bio/blog/building-better-benchmarks-for-adme-optimization. ↗
194. Kausik Hira, Mohd Zaki, Dhruvil Sheth, Mausam, and N M Anoop Krishnan. Reconstructing the materials tetrahedron: challenges in materials information extraction. Digit. Discov., 3(5):1021–1037, 2024. ISSN 2635-098X. doi:10.1039/d4dd00032c. URL https://pubs.rsc.org/en/content/articlelanding/2024/dd/ d… — [doi:10.1039/d4dd00032c](https://doi.org/10.1039/d4dd00032c) ↗
195. John Dagdelen, Alexander Dunn, Sanghoon Lee, Nicholas Walker, Andrew S Rosen, Gerbrand Ceder, Kristin A Persson, and Anubhav Jain. Structured information extraction from scientific text with large language models. Nat. Commun., 15(1):1418, February 2024. ISSN 2041-1723,2041-1723. doi:10.1038/s41467-…
196. Defne Circi, Ghazal Khalighinejad, Anlan Chen, Bhuwan Dhingra, and L Catherine Brinson. How well do large language models understand tables in materials science? Integr. Mater. Manuf. Innov., 13(3):669–687, September 2024. ISSN 2193-9764,2193-9772. doi:10.1007/s40192-024-00362-6. URL https://link.sp… — [doi:10.1007/s40192-024-00362-6](https://doi.org/10.1007/s40192-024-00362-6) ↗
197. Jon M Laurent, Joseph D Janizek, Michael Ruzo, Michaela M Hinks, Michael J Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D White, and Samuel G Rodriques. LAB-bench: Measuring capabilities of language models for biology research. arXiv [cs.AI], July 2024. URL http://arxiv.org/abs/2407.… ↗
198. Nawaf Alampara, Santiago Miret, and Kevin Maik Jablonka. MatText: Do language models need more than text & scale for materials modeling? arXiv [cond-mat.mtrl-sci], June 2024. URL http://arxiv.org/abs/2406. 17295. ↗
199. Yu Song, Santiago Miret, and Bang Liu. MatSci-NLP: Evaluating scientific language models on materials science language tasks using text-to-schema modeling. arXiv [cs.CL], May 2023. URL http://arxiv.org/ abs/2305.08264. ↗
200. Mohd Zaki, Jayadeva, Mausam, and N M Anoop Krishnan. MaScQA: investigating materials science knowledge of large language models. Digital Discovery, 3(2):313–327, 2024. doi:10.1039/D3DD00188A. URL https://pubs.rsc.org/en/content/articlehtml/2024/dd/d3dd00188a. — [doi:10.1039/d3dd00188a](https://doi.org/10.1039/d3dd00188a) ↗
201. Afnan Sultan, Jochen Sieg, Miriam Mathea, and Andrea Volkamer. Transformers for molecular property prediction: Lessons learned from the past five years. arXiv [cs.LG], April 2024. URL http://arxiv.org/ abs/2404.03969. — [arXiv:2404.03969](https://arxiv.org/abs/2404.03969) ↗
202. Janghoon Ock, Chakradhar Guntuboina, and Amir Barati Farimani. Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models. ACS CATALYSIS, 13(24):16032– 16044, November 2023. ISSN 2155-5435. doi:10.1021/acscatal.3c04956. — [doi:10.1021/acscatal.3c04956](https://doi.org/10.1021/acscatal.3c04956) find
203. Atakan Yuksel, Erva Ulusoy, Atabey Ünlü, and Tunca Doğan. SELFormer: Molecular representation learning via SELFIES language models. arXiv [q-bio.QM], April 2023. URL http://arxiv.org/abs/2304.04662. — [arXiv:2304.04662](https://arxiv.org/abs/2304.04662) ↗
204. Michiko Yoshitake, Fumitaka Sato, Hiroyuki Kawano, and Hiroshi Teraoka. MaterialBERT for natural language processing of materials science texts. Science and Technology of Advanced Materials: Methods, 2(1):372–380, December 2022. ISSN 2766-0400. doi:10.1080/27660400.2022.2124831. URL https://doi.org/… — [doi:10.1080/27660400.2022.2124831](https://doi.org/10.1080/27660400.2022.2124831) find
205. Jiahui Yu, Chengwei Zhang, Yingying Cheng, Yun-Fang Yang, Yuan-Bin She, Fengfan Liu, Weike Su, and An Su. SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes. July 2022. URL https://chemrxiv.org/engage/ ch… ↗
206. Samuel Boobier, David RJ Hose, A John Blacker, and Bao N Nguyen. Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. Nature communications, 11(1):5753, 2020. find
207. Zhi Hong, Aswathy Ajith, Gregory Pauloski, Eamon Duede, Kyle Chard, and Ian Foster. The diminishing returns of masked language models to science. arXiv [cs.CL], May 2022. URL http://arxiv.org/abs/2205.11342. ↗
208. Shu Huang and Jacqueline M. Cole. BatteryBERT: A Pretrained Language Model for Battery Database Enhancement. Journal of Chemical Information and Modeling, 62(24):6365–6377, December 2022. ISSN 1549-9596. doi:10.1021/acs.jcim.2c00035. URL https://doi.org/10.1021/acs.jcim.2c00035. Publisher: American… — [doi:10.1021/acs.jcim.2c00035](https://doi.org/10.1021/acs.jcim.2c00035) ↗
209. Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns (N Y), 3(4):100488,… — [doi:10.1016/j.patter.2022.100488](https://doi.org/10.1016/j.patter.2022.100488) find
210. Tanishq Gupta, Mohd Zaki, N M Anoop Krishnan, and Mausam. MatSciBERT: A materials domain language model for text mining and information extraction. npj Computational Materials, 8(1):1–11, May 2022. ISSN 2057-3960, 2057-3960. doi:10.1038/s41524-022-00784-w. URL https://www.nature.com/articles/ s41524… ↗
211. Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das. Large-scale chemical language representations capture molecular structure and properties. Nat. Mach. Intell., 4 (12):1256–1264, December 2022. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-022-00580-7. U… find
212. Jiang Guo, A Santiago Ibanez-Lopez, Hanyu Gao, Victor Quach, Connor W Coley, Klavs F Jensen, and Regina Barzilay. Automated chemical reaction extraction from scientific literature. J. Chem. Inf. Model., 62(9):2035–2045, May 2022. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.1c00284. URL http://d… find
213. Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed. Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv [cs.LG], November 2020. URL http://arxiv.org/abs/2011.13230. — [arXiv:2011.13230](https://arxiv.org/abs/2011.13230) ↗
214. Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, and Raghav Mani. BioMegatron: Larger biomedical domain language model. arXiv [cs.CL], October 2020. URL http://arxiv.org/abs/2010.06060. ↗
215. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-Specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthcare, 3(1):1–23, October 2021. doi:10.1145/3458754. URL https://…
216. ukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrz˛ebski. Molecule attention transformer. arXiv [cs.LG], February 2020. URL http://arxiv.org/abs/2002.08264. — [arXiv:2002.08264](https://arxiv.org/abs/2002.08264) ↗
217. Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’19, pages 429–436, N… — [doi:10.1145/3307339.3342186](https://doi.org/10.1145/3307339.3342186) find
218. Yifan Peng, Shankai Yan, and Zhiyong Lu. Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets. arXiv [cs.CL], June 2019. URL http://arxiv.org/abs/1906.05474. ↗
219. Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv [cs.CL], April 2019. URL http://arxiv.org/abs/1904.05342. ↗
220. Iz Beltagy, Kyle Lo, and Arman Cohan. SciBERT: A pretrained language model for scientific text. arXiv [cs.CL], March 2019. URL http://arxiv.org/abs/1903.10676. ↗
221. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, February 2020. ISSN 1367-4803, 1367-4811. doi:10.1093/bioinformatics/btz682. UR…
222. Di Wu, Qi Chen, Xiaojie Chen, Feng Han, Zhong Chen, and Yi Wang. The blood–brain barrier: structure, regulation, and drug delivery. Signal Transduction and Targeted Therapy, 8(1):1–27, May 2023. ISSN 2059-3635. doi:10.1038/s41392-023-01481-w. URL https://www.nature.com/articles/s41392-023-01481-w. P… — [doi:10.1038/s41392-023-01481-w](https://doi.org/10.1038/s41392-023-01481-w) ↗
223. Caterina Bissantz, Bernd Kuhn, and Martin Stahl. A medicinal chemist’s guide to molecular interactions. Journal of medicinal chemistry, 53(14):5061–5084, 2010. find
224. Stephen D Roughley and Allan M Jordan. The medicinal chemist’s toolbox: an analysis of reactions used in the pursuit of drug candidates. Journal of medicinal chemistry, 54(10):3451–3479, 2011. find
225. Irini Doytchinova. Drug Design—Past, Present, Future. Molecules, 27(5):1496, February 2022. ISSN 1420-3049. doi:10.3390/molecules27051496. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911833/. — [doi:10.3390/molecules27051496](https://doi.org/10.3390/molecules27051496) ↗
226. Introduction to Physical Polymer Science, 4th Edition \| Wiley. URL https://www.wiley.com/en-us/ Introduction+to+Physical+Polymer+Science%2C+4th+Edition-p-9780471706069. ↗
227. David J. Newman and Gordon M. Cragg. Natural Products as Sources of New Drugs from 1981 to 2014. Journal of Natural Products, 79(3):629–661, March 2016. ISSN 0163-3864. doi:10.1021/acs.jnatprod.5b01055. URL https://doi.org/10.1021/acs.jnatprod.5b01055. Publisher: American Chemical Society. — [doi:10.1021/acs.jnatprod.5b01055](https://doi.org/10.1021/acs.jnatprod.5b01055) ↗
228. Paulo Michel Pinheiro Ferreira, Daniel Dias Rufino Arcanjo, and Ana Paula Peron. Drug development, Brazilian biodiversity and political choices: Where are we heading? Journal of Toxicology and Environmental Health, Part B, 26(5):257–274, July 2023. ISSN 1093-7404. doi:10.1080/10937404.2023.2193762.… — [doi:10.1080/10937404.2023.2193762](https://doi.org/10.1080/10937404.2023.2193762) find
229. Hartmuth C Kolb and K. Barry Sharpless. The growing impact of click chemistry on drug discovery. Drug Discovery Today, 8(24):1128–1137, December 2003. ISSN 1359-6446. doi:10.1016/S1359-6446(03)02933-7. URL https://www.sciencedirect.com/science/article/pii/S1359644603029337. — [doi:10.1016/s1359-6446(03)02933-7](https://doi.org/10.1016/s1359-6446%2803%2902933-7) ↗
230. Nathan J. Castellino, Andrew P. Montgomery, Jonathan J. Danon, and Michael Kassiou. Late-stage Functionalization for Improving Drug-like Molecular Properties. Chemical Reviews, 123(13):8127–8153, July 2023. ISSN 0009-2665. doi:10.1021/acs.chemrev.2c00797. URL https://doi.org/10.1021/acs.chemrev.2c00… — [doi:10.1021/acs.chemrev.2c00797](https://doi.org/10.1021/acs.chemrev.2c00797) ↗
231. Komal Sharma, Krishna K. Sharma, Anku Sharma, and Rahul Jain. Peptide-based drug discovery: Current status and recent advances. Drug Discovery Today, 28(2):103464, February 2023. ISSN 1359-6446. doi:10.1016/j.drudis.2022.103464. URL https://www.sciencedirect.com/science/article/ pii/S135964462200457… — [doi:10.1016/j.drudis.2022.103464](https://doi.org/10.1016/j.drudis.2022.103464) ↗
232. Brandon J. Reizman and Klavs F. Jensen. Feedback in Flow for Accelerated Reaction Development. Accounts of Chemical Research, 49(9):1786–1796, September 2016. ISSN 0001-4842. doi:10.1021/acs.accounts.6b00261. URL https://doi.org/10.1021/acs.accounts.6b00261. Publisher: American Chemical Society. — [doi:10.1021/acs.accounts.6b00261](https://doi.org/10.1021/acs.accounts.6b00261) ↗
233. Joseph A. DiMasi, Henry G. Grabowski, and Ronald W. Hansen. Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47:20–33, May 2016. ISSN 1879-1646. doi:10.1016/j.jhealeco.2016.01.012. — [doi:10.1016/j.jhealeco.2016.01.012](https://doi.org/10.1016/j.jhealeco.2016.01.012) find
234. Errol G Lewars. Computational chemistry: Introduction to the theory and applications of molecular and quantum mechanics. Springer International Publishing, Cham, Switzerland, 4 edition, April 2024. ISBN 9783031514425,9783031514432. doi:10.1007/978-3-031-51443-2. URL https://link.springer.com/ book/1… — [doi:10.1007/978-3-031-51443-2](https://doi.org/10.1007/978-3-031-51443-2) ↗
235. Xavier Bidault and Santanu Chaudhuri. How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals? Molecules, 28(11):4471, January 2023. ISSN 1420-3049. doi:10.3390/molecules28114471. URL https://www.mdpi.com/1420-3049/28/11/4471. Number: 11 Publisher: Multidisciplinary Digi… — [doi:10.3390/molecules28114471](https://doi.org/10.3390/molecules28114471) ↗
236. Edward O. Pyzer-Knapp, Linjiang Chen, Graeme M. Day, and Andrew I. Cooper. Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps. Science Advances, 7 (33):eabi4763, August 2021. doi:10.1126/sciadv.abi4763. URL https://www.science.org/doi/… — [doi:10.1126/sciadv.abi4763](https://doi.org/10.1126/sciadv.abi4763) ↗
237. Scott Fredericks, Kevin Parrish, Dean Sayre, and Qiang Zhu. PyXtal: A Python library for crystal structure generation and symmetry analysis. Computer Physics Communications, 261:107810, April 2021. ISSN 0010-4655. doi:10.1016/j.cpc.2020.107810. URL https://www.sciencedirect.com/science/article/pii/… — [doi:10.1016/j.cpc.2020.107810](https://doi.org/10.1016/j.cpc.2020.107810) ↗
238. David H. Case, Josh E. Campbell, Peter J. Bygrave, and Graeme M. Day. Convergence Properties of Crystal Structure Prediction by Quasi-Random Sampling. Journal of Chemical Theory and Computation, 12(2):910–924, February 2016. ISSN 1549-9618. doi:10.1021/acs.jctc.5b01112. URL https://doi.org/10.1021/a… — [doi:10.1021/acs.jctc.5b01112](https://doi.org/10.1021/acs.jctc.5b01112) find
239. A. V. Kazantsev, P. G. Karamertzanis, C. S. Adjiman, and C. C. Pantelides. Efficient Handling of Molecular Flexibility in Lattice Energy Minimization of Organic Crystals. Journal of Chemical Theory and Computation, 7(6):1998–2016, June 2011. ISSN 1549-9618. doi:10.1021/ct100597e. URL https://doi.org… — [doi:10.1021/ct100597e](https://doi.org/10.1021/ct100597e) ↗
240. Guannan Huang, Yani Guo, Ye Chen, and Zhengwei Nie. Application of Machine Learning in Material Synthesis and Property Prediction. Materials, 16(17):5977, January 2023. ISSN 1996-1944. doi:10.3390/ma16175977. URL https://www.mdpi.com/1996-1944/16/17/5977. Number: 17 Publisher: Multidisciplinary Digi… — [doi:10.3390/ma16175977](https://doi.org/10.3390/ma16175977) ↗
241. Karina Martinez-Mayorga, José G. Rosas-Jiménez, Karla Gonzalez-Ponce, Edgar López-López, Antonio Neme, and José L. Medina-Franco. The pursuit of accurate predictive models of the bioactivity of small molecules. Chemical Science, 15(6):1938–1952, February 2024. ISSN 2041-6539. doi:10.1039/D3SC05534E.… — [doi:10.1039/d3sc05534e](https://doi.org/10.1039/d3sc05534e) find
242. Geemi P Wellawatte, Heta A Gandhi, Aditi Seshadri, and Andrew D White. A PERSPECTIVE ON EXPLA-NATIONS OF MOLECULAR PREDICTION MODELS. J. Chem. Theory Comput., 19(8):2149–2160, April 2023. ISSN 1549-9618, 1549-9626. doi:10.1021/acs.jctc.2c01235. URL http://dx.doi.org/10.1021/acs. jctc.2c01235. — [doi:10.1021/acs.jctc.2c01235](https://doi.org/10.1021/acs.jctc.2c01235) ↗
243. Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, and Michael Bendersky. LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models. In Companion Proceedings of the ACM Web Conference 2023, WWW ’23 Companion, pages 1014–1019, New York, NY, USA, April 2023. Association… — [doi:10.1145/3543873.3587605](https://doi.org/10.1145/3543873.3587605) find
244. P Schwaller, Daniel Probst, A Vaucher, Vishnu H Nair, D Kreutter, T Laino, and J Reymond. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell., 3(2):144–152, August 2020. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-020-00284-w. URL https://www.nature.com… — [doi:10.1038/s42256-020-00284-w](https://doi.org/10.1038/s42256-020-00284-w) ↗
245. Alessandra Toniato, Alain C Vaucher, Philippe Schwaller, and Teodoro Laino. Enhancing diversity in language based models for single-step retrosynthesis. Digital Discovery, 2(2):489–501, April 2023. ISSN 2635-098X. doi:10.1039/D2DD00110A. URL https://pubs.rsc.org/en/content/articlelanding/ 2023/dd/d2… — [doi:10.1039/d2dd00110a](https://doi.org/10.1039/d2dd00110a) ↗
246. Philippe Schwaller, Benjamin Hoover, Jean-Louis Reymond, Hendrik Strobelt, and Teodoro Laino. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Science Advances, 7 (15):eabe4166, April 2021. doi:10.1126/sciadv.abe4166. URL https://www.science.org/doi/10.1126/… — [doi:10.1126/sciadv.abe4166](https://doi.org/10.1126/sciadv.abe4166) ↗
247. Philippe Schwaller, Alain C Vaucher, Teodoro Laino, and Jean-Louis Reymond. Prediction of chemical reaction yields using deep learning. Mach. Learn. Sci. Technol., 2(1):015016, March 2021. ISSN 2632-2153. doi:10.1088/2632-2153/abc81d. URL https://iopscience.iop.org/article/10.1088/2632-2153/ abc81d/… — [doi:10.1088/2632-2153/abc81d](https://doi.org/10.1088/2632-2153/abc81d) ↗
248. Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’19, pages 429–436, N… — [doi:10.1145/3307339.3342186](https://doi.org/10.1145/3307339.3342186) find
249. Xiao-Chen Zhang, Cheng-Kun Wu, Jia-Cai Yi, Xiang-Xiang Zeng, Can-Qun Yang, Ai-Ping Lu, Hou T-j, Ting-Jun Hou, and Dong-Sheng Cao. Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration. Research, 2022:0004, December 2022… — [doi:10.34133/research.0004](https://doi.org/10.34133/research.0004) find
250. Guoli Xiong, Zhenxing Wu, Jiacai Yi, Li Fu, Zhijiang Yang, Changyu Hsieh, Mingzhu Yin, Xiangxiang Zeng, Chengkun Wu, Aiping Lu, Xiang Chen, Tingjun Hou, and Dongsheng Cao. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res.,… — [doi:10.1093/nar/gkab255](https://doi.org/10.1093/nar/gkab255) find
251. Teague Sterling and John J. Irwin. ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11):2324–2337, November 2015. ISSN 1549-9596. doi:10.1021/acs.jcim.5b00559. URL https://doi.org/10.1021/acs.jcim.5b00559. Publisher: American Chemical Society. — [doi:10.1021/acs.jcim.5b00559](https://doi.org/10.1021/acs.jcim.5b00559) ↗
252. Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das. Large-scale chemical language representations capture molecular structure and properties. Nature Machine Intelligence, 4(12):1256–1264, December 2022. ISSN 2522-5839, 2522-5839. doi:10.1038/s42256-022-0… find
253. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoe… — Transformers: State-of-the-Art Natural Language Processing
254. Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay. Analyzing Learned Molecular Representations for Property Prediction. Journal… find
255. Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee. Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction. ACS Central Science, 5(9):1572–1583, September 2019. ISSN 2374-7943, 2374-7951. doi:10.1021/… — [doi:10.1021/acscentsci.9b00576](https://doi.org/10.1021/acscentsci.9b00576) find
256. Jesse Vig. Bertviz: A tool for visualizing multihead self-attention in the bert model. In ICLR workshop: Debugging machine learning models, volume 3, 2019. find
257. Michael A. Skinnider. Invalid SMILES are beneficial rather than detrimental to chemical language models. Nature Machine Intelligence, 6(4):437–448, April 2024. ISSN 2522-5839. doi:10.1038/s42256-024-00821-x. URL https://www.nature.com/articles/s42256-024-00821-x. Publisher: Nature Publishing Group. — [doi:10.1038/s42256-024-00821-x](https://doi.org/10.1038/s42256-024-00821-x) ↗
258. Florence H Vermeire and William H Green. Transfer learning for solvation free energies: From quantum chemistry to experiments. Chemical Engineering Journal, 418:129307, 2021. find
259. David L Mobley and J Peter Guthrie. FreeSolv: a database of experimental and calculated hydration free energies, with input files. J. Comput. Aided Mol. Des., 28(7):711–720, July 2014. ISSN 0920-654X,1573-4951. doi:10.1007/s10822-014-9747-x. URL https://link.springer.com/article/10. 1007/s10822-014-… — [doi:10.1007/s10822-014-9747-x](https://doi.org/10.1007/s10822-014-9747-x) ↗
260. Aleksandr V Marenich, Casey P Kelly, Jason D Thompson, Gregory D Hawkins, Candee C Chambers, David J Giesen, Paul Winget, Christopher J Cramer, and Donald G Truhlar. Minnesota solvation database (mnsol) version 2012. 2020. find
261. Edouard Moine, Romain Privat, Baptiste Sirjean, and Jean-Noël Jaubert. Estimation of solvation quantities from experimental thermodynamic data: Development of the comprehensive compsol databank for pure and mixed solutes. Journal of Physical and Chemical Reference Data, 46(3), 2017. find
262. Laura M Grubbs, Mariam Saifullah, E Nohelli, Shulin Ye, Sai S Achi, William E Acree Jr, and Michael H Abraham. Mathematical correlations for describing solute transfer into functionalized alkane solvents containing hydroxyl, ether, ester or ketone solvents. Fluid phase equilibria, 298(1):48–53, 2010… find
263. Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian P Kelley, Andrew Palmer, Volker Settels, et al. Are learned molecular representations ready for prime time? 2019. doi:10.26434/chemrxiv.7940594.v2. find
264. Yu Rong, Yatao Bian, Tingyang Xu, Wei-Yang Xie, Ying Wei, Wen-Bing Huang, and Junzhou Huang. Self-Supervised Graph Transformer on Large-Scale Molecular Data. Adv. Neural Inf. Process. Syst., 33:12559– 12571, June 2020. ISSN 1049-5258. URL https://proceedings.neurips.cc/paper_files/paper/ 2020/hash/9… ↗
265. Benedikt Winter, Clemens Winter, Johannes Schilling, and André Bardow. A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing. Digital Discovery, 1(6):859–869, December 2022. ISSN 2635-098X. doi:10.1039/D2DD00058J. URL https://pubs.rsc.org/en/… — [doi:10.1039/d2dd00058j](https://doi.org/10.1039/d2dd00058j) ↗
266. Jing Jiang, Yachao Li, Ruisheng Zhang, and Yunwu Liu. INTransformer: Data augmentation-based contrastive learning by injecting noise into transformer for molecular property prediction. Journal of Molecular Graphics and Modelling, 128:108703, May 2024. ISSN 1093-3263. doi:10.1016/j.jmgm.2024.108703.… — [doi:10.1016/j.jmgm.2024.108703](https://doi.org/10.1016/j.jmgm.2024.108703) find
267. Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar. Multi-modal molecule structure-text model for text-based retrieval and editing. arXiv [cs.LG], December 2022. URL http://arxiv.org/abs/2212.10789. ↗
268. Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton. PubChem 2023 update. Nucleic Acids Res., 51(D1):D1373–D1380, January 2023. ISSN 0305-1048, 1362-4962. doi:10.1093/nar/… — [doi:10.1093/nar/gkac956](https://doi.org/10.1093/nar/gkac956) find
269. Minghao Xu, Xinyu Yuan, Santiago Miret, and Jian Tang. ProtST: Multi-modality learning of protein sequences and biomedical texts. In International Conference on Machine Learning, pages 38749–38767. PMLR, July 2023. URL https://proceedings.mlr.press/v202/xu23t.html. ↗
270. Glen M Hocky. Connecting molecular properties with plain language. Nat. Mach. Intell., 6(3):249–250, March 2024. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-024-00812-y. URL https://www.nature.com/ articles/s42256-024-00812-y. — [doi:10.1038/s42256-024-00812-y](https://doi.org/10.1038/s42256-024-00812-y) ↗
271. Juan Manuel Zambrano Chaves, Eric Wang, Tao Tu, Eeshit Dhaval Vaishnav, Byron Lee, S Sara Mahdavi, Christopher Semturs, David Fleet, Vivek Natarajan, and Shekoofeh Azizi. Tx-LLM: A large language model for therapeutics. arXiv [cs.CL], June 2024. URL http://arxiv.org/abs/2406.06316. ↗
272. Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D Manning. BioMedLM: A 2.7B parameter language model trained on biomedical text. arXiv [cs.CL], March 2024. URL http://arxiv.org/a… ↗
273. Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning, and Huan Sun. LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset, April 2024. URL http://arxiv.org/abs/2402.09391. arXiv:2402.09391 [cs]. ↗
274. Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. BioMistral: A collection of open-source pretrained large language models for medical domains. arXiv [cs.CL], February 2024. URL http://arxiv.org/abs/2402.10373. ↗
275. Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, and Hisham Cholakkal. BiMediX: Bilingual medical mixture of experts LLM. arXiv [cs.CL], February 2024. URL http://arxiv.org/abs/2402.13253. ↗
276. Xuyang Zhao, Qibin Zhao, and Toshihisa Tanaka. EpilepsyLLM: Domain-specific large language model fine-tuned with epilepsy medical knowledge. arXiv [cs.CL], January 2024. URL http://arxiv.org/abs/2401. 05908. ↗
277. Zhihong Chen, Maya Varma, Jean-Benoit Delbrouck, Magdalini Paschali, Louis Blankemeier, Dave Van Veen, Jeya Maria Jose Valanarasu, Alaa Youssef, Joseph Paul Cohen, Eduardo Pontes Reis, Emily B Tsai, Andrew Johnston, Cameron Olsen, Tanishq Mathew Abraham, Sergios Gatidis, Akshay S Chaudhari, and Curt… — [arXiv:2401.12208](https://arxiv.org/abs/2401.12208) find
278. Gregory W. Kyro, Anton Morgunov, Rafael I. Brent, and Victor S. Batista. ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation. Journal of Chemical Information and Modeling, 64(3):653–665, February 2024. ISSN 1549-9596. doi:10.1021/acs.jcim.3c01456. U… — [doi:10.1021/acs.jcim.3c01456](https://doi.org/10.1021/acs.jcim.3c01456) find
279. Yizhen Luo, Jiahuan Zhang, Siqi Fan, Kai Yang, Yushuai Wu, Mu Qiao, and Zaiqing Nie. BioMedGPT: Open multimodal generative pre-trained transformer for BioMedicine. arXiv [cs.CE], August 2023. URL https://github.com/PharMolix/OpenBioMed. ↗
280. Tong Xie, Yuwei Wan, Wei Huang, Zhenyu Yin, Yixuan Liu, Shaozhou Wang, Qingyuan Linghu, Chunyu Kit, Clara Grazian, Wenjie Zhang, Imran Razzak, and Bram Hoex. DARWIN series: Domain specific large language models for natural science. arXiv [cs.CL], August 2023. URL http://arxiv.org/abs/2308.13565. ↗
281. Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. PMC-LLaMA: Towards building open-source language models for medicine. arXiv [cs.CL], April 2023. URL http://arxiv.org/ abs/2304.14454. ↗
282. Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform., 23(6), November 2022. ISSN 1467-5463, 1477-4054. doi:10.1093/bib/bbac409. URL http://dx.doi.org/10.1093/bib… ↗
283. Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, and Vijay Gadepally. Neural scaling of deep chemical models. Nature Machine Intelligence, 5(11):1297–1305, November 2023. ISSN 2522-5839. doi:10.1038/s42256-023-00740-3. URL https://www.nature.co… — [doi:10.1038/s42256-023-00740-3](https://doi.org/10.1038/s42256-023-00740-3) ↗
284. Viraj Bagal, Rishal Aggarwal, P K Vinod, and U Deva Priyakumar. MolGPT: Molecular generation using a Transformer-Decoder model. J. Chem. Inf. Model., 62(9):2064–2076, May 2022. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.1c00600. URL http://dx.doi.org/10.1021/acs.jcim.1c00600. — [doi:10.1021/acs.jcim.1c00600](https://doi.org/10.1021/acs.jcim.1c00600) ↗
285. Sanjar Adilov. Generative Pre-Training from molecules. ChemRxiv, September 2021. doi:10.26434/chemrxiv-2021-5fwjd. URL https://chemrxiv.org/engage/chemrxiv/ article-details/6142f60742198e8c31782e9e. — [doi:10.26434/chemrxiv-2021-5fwjd](https://doi.org/10.26434/chemrxiv-2021-5fwjd) ↗
286. Jesse W.-H. Li and John C. Vederas. Drug Discovery and Natural Products: End of an Era or an Endless Frontier? Science, 325(5937):161–165, July 2009. doi:10.1126/science.1168243. URL https://www.science.org/ doi/abs/10.1126/science.1168243. Publisher: American Association for the Advancement of Scie… — [doi:10.1126/science.1168243](https://doi.org/10.1126/science.1168243) ↗
287. David J. Newman and Gordon M. Cragg. Natural Products As Sources of New Drugs over the 30 Years from 1981 to 2010. Journal of Natural Products, 75(3):311–335, March 2012. ISSN 0163-3864. doi:10.1021/np200906s. URL https://doi.org/10.1021/np200906s. Publisher: American Chemical Society. — [doi:10.1021/np200906s](https://doi.org/10.1021/np200906s) ↗
288. Maya A. Farha and Eric D. Brown. Strategies for target identification of antimicrobial natural products. Natural Product Reports, 33(5):668–680, 2016. doi:10.1039/C5NP00127G. URL https://pubs.rsc.org/ en/content/articlelanding/2016/np/c5np00127g. Publisher: Royal Society of Chemistry. — [doi:10.1039/c5np00127g](https://doi.org/10.1039/c5np00127g) ↗
289. AkshatKumar Nigam, Robert Pollice, Mario Krenn, Gabriel dos Passos Gomes, and Alán Aspuru-Guzik. Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES. Chemical Science, 12(20):7079–7090, 2021. doi:10.1039/D1SC0… — [doi:10.1039/d1sc00231g](https://doi.org/10.1039/d1sc00231g) find
290. Heta A Gandhi and Andrew D White. Explaining molecular properties with natural language. ChemRxiv, October 2022. doi:10.26434/chemrxiv-2022-v5p6m-v3. URL https://chemrxiv.org/engage/chemrxiv/ article-details/633731d1f764e6e535093041. — [doi:10.26434/chemrxiv-2022-v5p6m-v3](https://doi.org/10.26434/chemrxiv-2022-v5p6m-v3) ↗
291. Yuanqi Du, Arian R Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, and Tom L Blundell. Machine learning-aided generative molecular design. Nat. Mach. Intell., pages 1–16, June 2024. ISSN 2522-5839,2522-5839. doi:10.1038/s42256-024-00843-5. UR… — [doi:10.1038/s42256-024-00843-5](https://doi.org/10.1038/s42256-024-00843-5) find
292. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pages 2790–2799. PMLR, 2019. find
293. Addis S. Fuhr and Bobby G. Sumpter. Deep generative models for materials discovery and machine learning-accelerated innovation. FRONTIERS IN MATERIALS, 9, MAR 22 2022. ISSN 2296-8016. doi:10.3389/fmats.2022.865270. — [doi:10.3389/fmats.2022.865270](https://doi.org/10.3389/fmats.2022.865270) find
294. Ri Han, Hongryul Yoon, Gahee Kim, Hyundo Lee, and Yoonji Lee. Revolutionizing medicinal chemistry: The application of artificial intelligence (ai) in early drug discovery. PHARMACEUTICALS, 16(9), SEP 2023. doi:10.3390/ph16091259. — [doi:10.3390/ph16091259](https://doi.org/10.3390/ph16091259) find
295. Nikoletta-Maria Koutroumpa, Konstantinos D. Papavasileiou, Anastasios G. Papadiamantis, Georgia Melagraki, and Antreas Afantitis. A systematic review of deep learning methodologies used in the drug discovery process with emphasis on in vivo validation. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 24… — [doi:10.3390/ijms24076573](https://doi.org/10.3390/ijms24076573) find
296. Douglas B. Kell, Soumitra Samanta, and Neil Swainston. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. BIOCHEMICAL JOURNAL, 477 (23):4559–4580, DEC 2020. ISSN 0264-6021. doi:10.1042/BCJ20200781. — [doi:10.1042/bcj20200781](https://doi.org/10.1042/bcj20200781) find
297. Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen. Generative models for molecular discovery: Recent advances and challenges. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 12(5), SEP 2022. ISSN 1759-0876. doi:10.1002/wcms.1608. — [doi:10.1002/wcms.1608](https://doi.org/10.1002/wcms.1608) find
298. Amit Gangwal, Azim Ansari, Iqrar Ahmad, Abul Kalam Azad, Vinoth Kumarasamy, Vetriselvan Subra-maniyan, and Ling Shing Wong. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. FRONTIERS IN PHARMACOLOGY, 15, FEB 7 2024. doi:10.3389/fp… — [doi:10.3389/fphar.2024.1331062](https://doi.org/10.3389/fphar.2024.1331062) find
299. Martin Vogt. Using deep neural networks to explore chemical space. EXPERT OPINION ON DRUG DISCOVERY, 17(3):297–304, MAR 4 2022. ISSN 1746-0441. doi:10.1080/17460441.2022.2019704. — [doi:10.1080/17460441.2022.2019704](https://doi.org/10.1080/17460441.2022.2019704) find
300. Sekhar Talluri, Mohammad Amjad Kamal, and Rama Rao Malla. Novel computational methods for cancer drug design. CURRENT MEDICINAL CHEMISTRY, 31(5):554–572, 2024. ISSN 0929-8673. doi:10.2174/0929867330666230403100008. — [doi:10.2174/0929867330666230403100008](https://doi.org/10.2174/0929867330666230403100008) find
301. Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alán Aspuru-Guzik, and Alex Zhavoronkov. Molecu… — [doi:10.3389/fphar.2020.565644](https://doi.org/10.3389/fphar.2020.565644) find
302. Nathan Brown, Marco Fiscato, Marwin H S Segler, and Alain C Vaucher. GuacaMol: Benchmarking models for de novo molecular design. J. Chem. Inf. Model., 59(3):1096–1108, March 2019. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.8b00839. URL http://dx.doi.org/10.1021/acs.jcim.8b00839. — [doi:10.1021/acs.jcim.8b00839](https://doi.org/10.1021/acs.jcim.8b00839) ↗
303. Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer. Fréchet chemnet distance: a metric for generative models for molecules in drug discovery. Journal of chemical information and modeling, 58(9):1736–1741, 2018. find
304. Suhail Haroon, Hafsath C.a., and Jereesh A.s. Generative Pre-trained Transformer (GPT) based model with relative attention for de novo drug design. Computational Biology and Chemistry, 106:107911, October 2023. ISSN 1476-9271. doi:10.1016/j.compbiolchem.2023.107911. URL https://www.sciencedirect.com… — [doi:10.1016/j.compbiolchem.2023.107911](https://doi.org/10.1016/j.compbiolchem.2023.107911) ↗
305. Jianmin Wang, Jiashun Mao, Meng Wang, Xiangyang Le, and Yunyun Wang. Explore drug-like space with deep generative models. Methods, 210:52–59, February 2023. ISSN 1046-2023. doi:10.1016/j.ymeth.2023.01.004. URL https://www.sciencedirect.com/science/article/pii/S1046202323000129. — [doi:10.1016/j.ymeth.2023.01.004](https://doi.org/10.1016/j.ymeth.2023.01.004) ↗
306. Jiashun Mao, Jianmin Wang, Amir Zeb, Kwang-Hwi Cho, Haiyan Jin, Jongwan Kim, Onju Lee, Yunyun Wang, and Kyoung Tai No. Transformer-Based Molecular Generative Model for Antiviral Drug Design. Journal of Chemical Information and Modeling, 64(7):2733–2745, April 2024. ISSN 1549-9596. doi:10.1021/acs.jc… — [doi:10.1021/acs.jcim.3c00536](https://doi.org/10.1021/acs.jcim.3c00536) find
307. Wenyi Zhang, Kaiyue Zhang, and Jing Huang. A Simple Way to Incorporate Target Structural Information in Molecular Generative Models. Journal of Chemical Information and Modeling, 63(12):3719–3730, June 2023. ISSN 1549-9596. doi:10.1021/acs.jcim.3c00293. URL https://doi.org/10.1021/acs.jcim.3c00293.… — [doi:10.1021/acs.jcim.3c00293](https://doi.org/10.1021/acs.jcim.3c00293) ↗
308. Xun Wang, Changnan Gao, Peifu Han, Xue Li, Wenqi Chen, Alfonso Rodríguez Patón, Shuang Wang, and Pan Zheng. PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning. International Journal of Molecular Sciences, 24(2):1146, January 2023. ISSN 1422-0067. doi:10.3390/ijms2… — [doi:10.3390/ijms24021146](https://doi.org/10.3390/ijms24021146) find
309. Yasuhiro Yoshikai, Tadahaya Mizuno, Shumpei Nemoto, and Hiroyuki Kusuhara. A novel molecule generative model of VAE combined with Transformer for unseen structure generation, April 2024. URL http://arxiv.org/abs/2402.11950. arXiv:2402.11950 [physics, q-bio]. — [arXiv:2402.11950](https://arxiv.org/abs/2402.11950) ↗
310. Xiaoying Yan, Chi Gu, Yuehua Feng, and Jiaxin Han. Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism. Methods, 223:16–25, March 2024. ISSN 1046-2023. doi:10.1016/j.ymeth.2024.01.009. URL https://www.sciencedirect.com/science/article/pii/ S1046202324000276. — [doi:10.1016/j.ymeth.2024.01.009](https://doi.org/10.1016/j.ymeth.2024.01.009) ↗
311. Tao Shen, Jiale Guo, Zunsheng Han, Gao Zhang, Qingxin Liu, Xinxin Si, Dongmei Wang, Song Wu, and Jie Xia. AutoMolDesigner for Antibiotic Discovery: An AI-Based Open-Source Software for Automated Design of Small-Molecule Antibiotics. Journal of Chemical Information and Modeling, 64(3):575–583, Februa… — [doi:10.1021/acs.jcim.3c01562](https://doi.org/10.1021/acs.jcim.3c01562) find
312. G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90–98, 2012. find
313. Govindan Subramanian, Bharath Ramsundar, Vijay Pande, and Rajiah Aldrin Denny. Computational modeling of β-secretase 1 (bace-1) inhibitors using ligand based approaches. Journal of chemical information and modeling, 56(10):1936–1949, 2016. find
314. Guozhang Xu, Marta C Abad, Peter J Connolly, Michael P Neeper, Geoffrey T Struble, Barry A Springer, Stuart L Emanuel, Niranjan Pandey, Robert H Gruninger, Mary Adams, et al. 4-amino-6-arylamino-pyrimidine-5-carbaldehyde hydrazones as potent erbb-2/egfr dual kinase inhibitors. Bioorganic & medicinal… find
315. Leiye Yu, Licong He, Bing Gan, Rujuan Ti, Qingjie Xiao, Xin Yang, Hongli Hu, Lizhe Zhu, Sheng Wang, and Ruobing Ren. Structural insights into sphingosine-1-phosphate receptor activation. Proceedings of the National Academy of Sciences, 119(16):e2117716119, 2022. find
316. Peiyu Xu, Sijie Huang, Huibing Zhang, Chunyou Mao, X Edward Zhou, Xi Cheng, Icaro A Simon, Dan-Dan Shen, Hsin-Yung Yen, Carol V Robinson, et al. Structural insights into the lipid and ligand regulation of serotonin receptors. Nature, 592(7854):469–473, 2021. find
317. Jiangming Sun, Nina Jeliazkova, Vladimir Chupakhin, Jose-Felipe Golib-Dzib, Ola Engkvist, Lars Carlsson, Jörg Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, et al. Excape-db: an integrated large scale dataset facilitating big data analysis in chemogenomics. Journal of cheminformatics, 9:1–… find
318. Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang. Parameter-efficient fine-tuning for large models: A comprehensive survey. arXiv [cs.LG], March 2024. URL http://arxiv.org/abs/2403.14608. ↗
319. Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, and Maosong Sun. Parameter-efficient fine-tuning of large-scale pre… — Parameter-efficient fine-tuning of large-scale pre-trained language models
320. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. arXiv [cs.CL], June 2021. URL http://arxiv.org/abs/2106.09685. ↗
321. Abimael Guzman-Pando, Graciela Ramirez-Alonso, Carlos Arzate-Quintana, and Javier Camarillo-Cisneros. Deep learning algorithms applied to computational chemistry. Molecular Diversity, December 2023. ISSN 1573-501X. doi:10.1007/s11030-023-10771-y. URL https://doi.org/10.1007/s11030-023-10771-y. — [doi:10.1007/s11030-023-10771-y](https://doi.org/10.1007/s11030-023-10771-y) ↗
322. Jenna C. Fromer and Connor W. Coley. Computer-aided multi-objective optimization in small molecule discovery. Patterns, 4(2):100678, February 2023. ISSN 2666-3899. doi:10.1016/j.patter.2023.100678. URL https://www.sciencedirect.com/science/article/pii/S2666389923000016. — [doi:10.1016/j.patter.2023.100678](https://doi.org/10.1016/j.patter.2023.100678) ↗
323. Martin Vogt. Exploring chemical space — Generative models and their evaluation. Artificial Intelligence in the Life Sciences, 3:100064, December 2023. ISSN 2667-3185. doi:10.1016/j.ailsci.2023.100064. URL https://www.sciencedirect.com/science/article/pii/S2667318523000089. — [doi:10.1016/j.ailsci.2023.100064](https://doi.org/10.1016/j.ailsci.2023.100064) ↗
324. Manan Goel, Rishal Aggarwal, Bhuvanesh Sridharan, Pradeep Kumar Pal, and U. Deva Priyakumar. Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods. WIREs Computational Molecular Science, 13(2):e1637, 2023. ISSN 17… — [doi:10.1002/wcms.1637](https://doi.org/10.1002/wcms.1637) find
325. Shion Honda, Shoi Shi, and Hiroki R Ueda. SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv [cs.LG], November 2019. URL http://arxiv.org/abs/1911.04738. — [arXiv:1911.04738](https://arxiv.org/abs/1911.04738) ↗
326. Tatsuya Sagawa and Ryosuke Kojima. ReactionT5: a large-scale pre-trained model towards application of limited reaction data. arXiv [physics.chem-ph], November 2023. URL http://arxiv.org/abs/2311.06708. — [arXiv:2311.06708](https://arxiv.org/abs/2311.06708) ↗
327. Yin Fang, Ningyu Zhang, Zhuo Chen, Lingbing Guo, Xiaohui Fan, and Huajun Chen. Domain-agnostic molecular generation with chemical feedback. arXiv [cs.LG], January 2023. URL http://arxiv.org/abs/2301.11259. — [arXiv:2301.11259](https://arxiv.org/abs/2301.11259) ↗
328. Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. Unifying molecular and textual representations via multi-task language modelling. arXiv [cs.LG], January 2023. URL http://arxiv.org/abs/2301.12586. — [arXiv:2301.12586](https://arxiv.org/abs/2301.12586) ↗
329. Alain C Vaucher, Federico Zipoli, Joppe Geluykens, Vishnu H Nair, Philippe Schwaller, and Teodoro Laino. Automated extraction of chemical synthesis actions from experimental procedures. Nat. Commun., 11(1): 3601, July 2020. ISSN 2041-1723. doi:10.1038/s41467-020-17266-6. URL http://dx.doi.org/10.103… — [doi:10.1038/s41467-020-17266-6](https://doi.org/10.1038/s41467-020-17266-6) find
330. Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation between molecules and natural language. arXiv [cs.CL], April 2022. URL http://arxiv.org/abs/2204.11817. ↗
331. Carl Edwards, ChengXiang Zhai, and Heng Ji. Text2Mol: Cross-modal molecule retrieval with natural language queries. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 595…
332. Ryan A. Shenvi. Natural product synthesis in the 21st century: Beyond the mountain top. ACS Central Science, 10(3):519–528, 2024. doi:10.1021/acscentsci.3c01518. URL https://doi.org/10.1021/acscentsci. 3c01518. — [doi:10.1021/acscentsci.3c01518](https://doi.org/10.1021/acscentsci.3c01518) ↗
333. Qianxiang Ai, Fanwang Meng, Jiale Shi, Brenden Pelkie, and Connor W. Coley. Extracting Structured Data from Organic Synthesis Procedures Using a Fine-Tuned Large Language Model, April 2024. URL https://chemrxiv.org/engage/chemrxiv/article-details/661064e921291e5d1d2bc860. ↗
334. E. J. Corey. Robert Robinson Lecture. Retrosynthetic thinking—essentials and examples. Chemical Society Reviews, 17(0):111–133, January 1988. ISSN 1460-4744. doi:10.1039/CS9881700111. URL https://pubs.rsc.org/en/content/articlelanding/1988/cs/cs9881700111. Publisher: The Royal Society of Chemistry. — [doi:10.1039/cs9881700111](https://doi.org/10.1039/cs9881700111) ↗
335. Jennie B. Nerenberg, Deborah T. Hung, Patricia K. Somers, and Stuart L. Schreiber. Total synthesis of the immunosuppressive agent (-)-discodermolide. Journal of the American Chemical Society, 115(26): 12621–12622, 1993. doi:10.1021/ja00079a066. URL https://doi.org/10.1021/ja00079a066. _eprint: https… — [doi:10.1021/ja00079a066](https://doi.org/10.1021/ja00079a066) ↗
336. Juno Nam and Jurae Kim. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions, December 2016. URL http://arxiv.org/abs/1612.09529. arXiv:1612.09529 [cs]. — [arXiv:1612.09529](https://arxiv.org/abs/1612.09529) ↗
337. Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models. ACS Central Science, 3(10):1103–1113, October 2017. ISSN 2374-7943. doi:10.102… — [doi:10.1021/acscentsci.7b00303](https://doi.org/10.1021/acscentsci.7b00303) find
338. Nadine Schneider, Nikolaus Stiefl, and Gregory A. Landrum. What’s what: The (nearly) definitive guide to reaction role assignment. Journal of Chemical Information and Modeling, 56(12):2336–2346, 2016. doi:10.1021/acs.jcim.6b00564. URL https://doi.org/10.1021/acs.jcim.6b00564. PMID: 28024398. — [doi:10.1021/acs.jcim.6b00564](https://doi.org/10.1021/acs.jcim.6b00564) ↗
339. Matthew Gunther10 August 2016. Software could revolutionise chemistry. URL https://www.chemistryworld.com/news/software-could-revolutionise-chemistry/1017236.article. ↗
340. Tomasz Klucznik, Barbara Mikulak-Klucznik, Michael P. McCormack, Heather Lima, Sara Szymkuć, Man-ishabrata Bhowmick, Karol Molga, Yubai Zhou, Lindsey Rickershauser, Ewa P. Gajewska, Alexei Toutchkine, Piotr Dittwald, Michał P. Startek, Gregory J. Kirkovits, Rafał Roszak, Ariel Adamski, Bianka Siere… — [doi:10.1016/j.chempr.2018.02.002](https://doi.org/10.1016/j.chempr.2018.02.002) find
341. Marwin H S Segler and Mark P Waller. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry, 23(25):5966–5971, May 2017. ISSN 0947-6539, 1521-3765. doi:10.1002/chem.201605499. URL https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/chem.201605499. — [doi:10.1002/chem.201605499](https://doi.org/10.1002/chem.201605499) ↗
342. Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, and Regina Barzilay. Learning Graph Models for Retrosynthesis Prediction, June 2021. URL http://arxiv.org/abs/2006.07038. arXiv:2006.07038 [cs, stat]. ↗
343. Geemi P Wellawatte and Philippe Schwaller. Extracting human interpretable structure-property relationships in chemistry using XAI and large language models. arXiv [physics.chem-ph], November 2023. URL http://arxiv.org/abs/2311.04047. — [arXiv:2311.04047](https://arxiv.org/abs/2311.04047) ↗
344. Andrea Cadeddu, Elizabeth K. Wylie, Janusz Jurczak, Matthew Wampler-Doty, and Bartosz A. Grzybowski. Organic Chemistry as a Language and the Implications of Chemical Linguistics for Structural and Retrosynthetic Analyses. Angewandte Chemie International Edition, 53(31):8108–8112, 2014. ISSN 1521-377… — [doi:10.1002/anie.201403708](https://doi.org/10.1002/anie.201403708) find
345. Philippe Schwaller, Théophile Gaudin, Dávid Lányi, Costas Bekas, and Teodoro Laino. “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chemical Science, 9(28):6091–6098, July 2018. ISSN 2041-6539. doi:10.1039/C8SC02339E. URL h… — [doi:10.1039/c8sc02339e](https://doi.org/10.1039/c8sc02339e) find
346. Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi Jaakkola. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network, December 2017. URL http://arxiv.org/abs/1709.04555. arXiv:1709.04555 [cs, stat]. ↗
347. John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, and José Miguel Hernández-Lobato. A Generative Model For Electron Paths, March 2019. URL http://arxiv.org/abs/1805.10970. arXiv:1805.10970 [physics, stat]. — [arXiv:1805.10970](https://arxiv.org/abs/1805.10970) ↗
348. Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H. Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, and Teodoro Laino. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chemical Science, 11(12):3316–332… — [doi:10.1039/c9sc05704h](https://doi.org/10.1039/c9sc05704h) find
349. Shuan Chen and Yousung Jung. Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention. JACS Au, 1(10):1612–1620, August 2021. ISSN 2691-3704. doi:10.1021/jacsau.1c00246. URL https://doi.org/10.1021/jacsau.1c00246. Publisher: American Chemical Society. — [doi:10.1021/jacsau.1c00246](https://doi.org/10.1021/jacsau.1c00246) ↗
350. Annie M. Westerlund, Siva Manohar Koki, Supriya Kancharla, Alessandro Tibo, Lakshidaa Saigiridha-ran, Mikhail Kabeshov, Rocio Mercado, and Samuel Genheden. Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis. JOURNAL OF CHEMICAL INFORMATION AND MODELIN… — [doi:10.1021/acs.jcim.3c01685](https://doi.org/10.1021/acs.jcim.3c01685) find
351. Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu, and Yuedong Yang. Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks. Journal of Chemical Information and Modeling, 60(1): 47–55, January 2020. ISSN 1549-9596. doi:10.1021/acs.jcim.9b00949. URL https://doi.org/10.… — [doi:10.1021/acs.jcim.9b00949](https://doi.org/10.1021/acs.jcim.9b00949) find
352. Daniel Mark Lowe. Extraction of chemical structures and reactions from the literature. October 2012. URL http://www.dspace.cam.ac.uk/handle/1810/244727. ↗
353. Junren Li, Lei Fang, and Jian-Guang Lou. Retro-BLEU: quantifying chemical plausibility of retrosynthesis routes through reaction template sequence analysis. Digital Discovery, 3(3):482–490, 2024. doi:10.1039/D3DD00219E. URL https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00219e. Publisher… — [doi:10.1039/d3dd00219e](https://doi.org/10.1039/d3dd00219e) ↗
354. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. BLEU: a method for automatic evaluation of machine translation. In Pierre Isabelle, Eugene Charniak, and Dekang Lin, editors, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL ’02, pages 311–318, Phi…
355. Chin-Yew Lin. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics. URL https://aclanthology.org/W04-1013. ↗
356. Laurianne David, Amol Thakkar, Rocío Mercado, and Ola Engkvist. Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminform., 12(1):56, September 2020. ISSN 1758-2946. doi:10.1186/s13321-020-00460-5. URL https://doi.org/10.1186/s13321-020-00460-5. — [doi:10.1186/s13321-020-00460-5](https://doi.org/10.1186/s13321-020-00460-5) ↗
357. Gus L W Hart, Tim Mueller, Cormac Toher, and Stefano Curtarolo. Machine learning for alloys. Nat. Rev. Mater., 6(8):730–755, July 2021. ISSN 2058-8437,2058-8437. doi:10.1038/s41578-021-00340-w. URL https://www.nature.com/articles/s41578-021-00340-w. — [doi:10.1038/s41578-021-00340-w](https://doi.org/10.1038/s41578-021-00340-w) ↗
358. Oscar Eraso, Daniela Bolaños, Nikolas Echeverri, Carolina Orozco Donneys, Tayebeh Ameri, and Jose Dario Perea. A present scenario of the computational approaches for ternary organic solar cells. J. Renew. Sustain. Energy, 15(6):062702, November 2023. ISSN 1941-7012. doi:10.1063/5.0172426. URL https:… — [doi:10.1063/5.0172426](https://doi.org/10.1063/5.0172426) find
359. Yu-Chen Lo, Stefano E Rensi, Wen Torng, and Russ B Altman. Machine learning in chemoinformatics and drug discovery. Drug Discov. Today, 23(8):1538–1546, August 2018. ISSN 1359-6446,1878-5832. doi:10.1016/j.drudis.2018.05.010. URL http://dx.doi.org/10.1016/j.drudis.2018.05.010. — [doi:10.1016/j.drudis.2018.05.010](https://doi.org/10.1016/j.drudis.2018.05.010) ↗
360. Daniel Flam-Shepherd and Alán Aspuru-Guzik. Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files. arXiv [cs.LG], May 2023. URL http://arxiv.org/abs/2305.05708. — [arXiv:2305.05708](https://arxiv.org/abs/2305.05708) ↗
361. Kohulan Rajan, Achim Zielesny, and Christoph Steinbeck. DECIMER 1.0: deep learning for chemical image recognition using transformers. J. Cheminform., 13(1):61, August 2021. ISSN 1758-2946,1758-2946. doi:10.1186/s13321-021-00538-8. URL http://dx.doi.org/10.1186/s13321-021-00538-8. — [doi:10.1186/s13321-021-00538-8](https://doi.org/10.1186/s13321-021-00538-8) ↗
362. J. T. Carstensen and Faraneh Attarchi. Decomposition of Aspirin in the Solid State in the Presence of Limited Amounts of Moisture III: Effect of Temperature and a Possible Mechanism. Journal of Pharmaceutical Sciences, 77(4):318–321, April 1988. ISSN 0022-3549. doi:10.1002/jps.2600770407. URL https:… — [doi:10.1002/jps.2600770407](https://doi.org/10.1002/jps.2600770407) find
363. Philipp Seidl, Andreu Vall, Sepp Hochreiter, and Guenter Klambauer. Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. Arxiv, 2023. find
364. Hanwen Xu, Addie Woicik, Hoifung Poon, Russ B. Altman, and Sheng Wang. Multilingual translation for zero-shot biomedical classification using BioTranslator. NATURE COMMUNICATIONS, 14(1), February 2023. ISSN 2041-1723. doi:10.1038/s41467-023-36476-2. find
365. Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, and Chaowei Xiao. ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback. Arxiv, 2023. find
366. Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and Tie-Yan Liu. MolXPT: Wrapping Molecules with Text for Generative Pre-training. Arxiv, 2023. find
367. Pengfei Liu, Yiming Ren, Jun Tao, and Zhixiang Ren. Git-mol: A multi-modal large language model for molecular science with graph, image, and text. Computers in Biology and Medicine, 171:108073, March 2024. ISSN 0010-4825. doi:10.1016/j.compbiomed.2024.108073. URL http://dx.doi.org/10.1016/j. compbio… find
368. Junfeng Fang, Shuai Zhang, Chang Wu, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, and Xiang Wang. MolTC: Towards Molecular Relational Modeling In Language Models. Arxiv, 2024. find
369. Haohui Zhang, Juntong Wu, Shichao Liu, and Shen Han. A pre-trained multi-representation fusion network for molecular property prediction. INFORMATION FUSION, 103, March 2024. ISSN 1566-2535. doi:10.1016/j.inffus.2023.102092. — [doi:10.1016/j.inffus.2023.102092](https://doi.org/10.1016/j.inffus.2023.102092) find
370. Huaisheng Zhu, Teng Xiao, and Vasant G Honavar. 3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs. Arxiv, 2024. find
371. Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, and Xun Wang. DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation. BRIEFINGS IN FUNCTIONAL GENOMICS, April 2024. ISSN… — [doi:10.1093/bfgp/elae011](https://doi.org/10.1093/bfgp/elae011) find
372. Peng Zhou, Jianmin Wang, Chunyan Li, Zixu Wang, Yiping Liu, Siqi Sun, Jianxin Lin, Longyue Wang, and Xiangxiang Zeng. Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model. Arxiv, 2024. find
373. Haisong Gong, Qiang Liu, Shu Wu, and Liang Wang. Text-Guided Molecule Generation with Diffusion Language Model. Arxiv, 2024. find
374. Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez, Renato Cerqueira, Dan Sanders, Kristin Schmidt, and Dmitry Zubarev. Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction, June 2023. URL http://arxiv.org/abs/2306.14919. arXiv:2306.14919 [physics… — [arXiv:2306.14919](https://arxiv.org/abs/2306.14919) ↗
375. Michael Riedl, Sayak Mukherjee, and Mitch Gauthier. Descriptor-free deep learning QSAR model for the fraction unbound in human plasma. Mol. Pharm., 20(10):4984–4993, October 2023. ISSN 1543-8384,1543-8392. doi:10.1021/acs.molpharmaceut.3c00129. URL http://dx.doi.org/10.1021/acs.molpharmaceut. 3c0012… — [doi:10.1021/acs.molpharmaceut.3c00129](https://doi.org/10.1021/acs.molpharmaceut.3c00129) ↗
376. MIMIC-III documentation. https://mimic.mit.edu/docs/iii/, 2021. URL https://mimic.mit.edu/ docs/iii/. Accessed: 2024-3-25. ↗
377. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A robustly optimized BERT pretraining approach. arXiv [cs.CL], July 2019. URL http://arxiv.org/abs/1907.11692. ↗
378. Hannah Smith, Zeyu Zhang, John Culnan, and Peter Jansen. ScienceExamCER: A high-density fine-grained science-domain corpus for common entity recognition. arXiv [cs.CL], November 2019. URL http://arxiv.org/abs/1911.10436. ↗
379. Matthew C Swain and Jacqueline M Cole. ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature. J. Chem. Inf. Model., 56(10):1894–1904, October 2016. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.6b00207. URL http://dx.doi.org/10.1021/acs.jcim.6… — [doi:10.1021/acs.jcim.6b00207](https://doi.org/10.1021/acs.jcim.6b00207) ↗
380. Santiago Ibanez. chemie-turk: Mechanical turk on your own machine for chemical literature annotation. URL https://github.com/asibanez/chemie-turk. ↗
381. Wei Zhang, Qinggong Wang, Xiangtai Kong, Jiacheng Xiong, Shengkun Ni, Duanhua Cao, Buying Niu, Mingan Chen, Runze Zhang, Yitian Wang, Lehan Zhang, Xutong Li, Zhaoping Xiong, Qian Shi, Ziming Huang, Zunyun Fu, and Mingyue Zheng. Fine-tuning large language models for chemical text mining. ChemRxiv, Fe… — [doi:10.26434/chemrxiv-2023-k7ct5-v2](https://doi.org/10.26434/chemrxiv-2023-k7ct5-v2) find
382. Kexin Chen, Hanqun Cao, Junyou Li, Yuyang Du, Menghao Guo, Xin Zeng, Lanqing Li, Jiezhong Qiu, Pheng Ann Heng, and Guangyong Chen. An autonomous large language model agent for chemical literature data mining. arXiv [cs.IR], February 2024. URL http://arxiv.org/abs/2402.12993. — [arXiv:2402.12993](https://arxiv.org/abs/2402.12993) ↗
383. Xin Wang, Liangliang Huang, Shuozhi Xu, and Kun Lu. How does a generative large language model perform on domain-specific information extraction? – a comparison between GPT-4 and a rule-based method on band gap extraction. J. Chem. Inf. Model., October 2024. ISSN 1549-9596,1549-960X. doi:10.1021/acs… — [doi:10.1021/acs.jcim.4c00882](https://doi.org/10.1021/acs.jcim.4c00882) find
384. Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T Koch, José A Márquez, and Kevin Maik Jablonka. From text to insight: Large language models for materials science data extraction. arXiv [cond-mat.mtrl-sci], July 2024. URL http://arxiv.org/… — [arXiv:2407.16867](https://arxiv.org/abs/2407.16867) ↗
385. Pranav Shetty, Arunkumar Chitteth Rajan, Chris Kuenneth, Sonakshi Gupta, Lakshmi Prerana Panchumarti, Lauren Holm, Chao Zhang, and Rampi Ramprasad. A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing. NPJ Comput Mater, 9(1):52, Ap… find
386. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-LM: Training multi-billion parameter language models using model parallelism. arXiv [cs.CL], September 2019. URL http://arxiv.org/abs/1909.08053. ↗
387. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. SQuAD: 100,000+ questions for machine comprehension of text. arXiv [cs.CL], June 2016. URL http://arxiv.org/abs/1606.05250. ↗
388. Yifan Peng, Qingyu Chen, and Zhiyong Lu. An empirical study of multi-task learning on BERT for biomedical text mining. arXiv [cs.CL], May 2020. URL http://arxiv.org/abs/2005.02799. ↗
389. Johannes Welbl, Nelson F Liu, and Matt Gardner. Crowdsourcing multiple choice science questions. arXiv [cs.HC], July 2017. URL http://arxiv.org/abs/1707.06209. ↗
390. Yu Song, Santiago Miret, Huan Zhang, and Bang Liu. HoneyBee: Progressive instruction finetuning of large language models for materials science. arXiv [cs.CL], October 2023. URL http://arxiv.org/abs/2310. 08511. ↗
391. Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, and Bang Liu. HoneyComb: A flexible LLM-based agent system for materials science. arXiv [cs.CL], August 2024. URL http://arxiv.org/abs/2409.00135. ↗
392. Sören Auer, Dante A C Barone, Cassiano Bartz, Eduardo G Cortes, Mohamad Yaser Jaradeh, Oliver Karras, Manolis Koubarakis, Dmitry Mouromtsev, Dmitrii Pliukhin, Daniil Radyush, Ivan Shilin, Markus Stocker, and Eleni Tsalapati. The SciQA scientific question answering benchmark for scholarly knowledge.… — [doi:10.1038/s41598-023-33607-z](https://doi.org/10.1038/s41598-023-33607-z) find
393. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Preprints, May 2021. URL http://arxiv.org/abs/2009.13081. ↗
394. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. MedMCQA : A large-scale multi-subject multi-choice dataset for medical domain question answering. arXiv [cs.CL], March 2022. URL http://arxiv.org/abs/2203.14371. ↗
395. Cayque Monteiro Castro Nascimento and André Silva Pimentel. Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6):1649–1655, March 2023. ISSN 1549-9596. doi:10.1021/acs.jcim.3c00285. URL https://doi.org/10.1021/acs.jcim. 3c002… — [doi:10.1021/acs.jcim.3c00285](https://doi.org/10.1021/acs.jcim.3c00285) ↗
396. Tim Humphry and Amy L. Fuller. Potential ChatGPT Use in Undergraduate Chemistry Laboratories. JOURNAL OF CHEMICAL EDUCATION, 100(4):1434–1436, April 2023. ISSN 0021-9584. doi:10.1021/acs.jchemed.3c00006. — [doi:10.1021/acs.jchemed.3c00006](https://doi.org/10.1021/acs.jchemed.3c00006) find
397. Mary E. Emenike and Bright U. Emenike. Was This Title Generated by ChatGPT? Considerations for Artificial Intelligence Text-Generation Software Programs for Chemists and Chemistry Educators. JOURNAL OF CHEMICAL EDUCATION, 100(4):1413–1418, April 2023. ISSN 0021-9584. doi:10.1021/acs.jchemed.3c00063. — [doi:10.1021/acs.jchemed.3c00063](https://doi.org/10.1021/acs.jchemed.3c00063) find
398. Suzanne Fergus, Michelle Botha, and Mehrnoosh Ostovar. Evaluating Academic Answers Generated Using ChatGPT. JOURNAL OF CHEMICAL EDUCATION, 100(4):1672–1675, April 2023. ISSN 0021-9584. doi:10.1021/acs.jchemed.3c00087. — [doi:10.1021/acs.jchemed.3c00087](https://doi.org/10.1021/acs.jchemed.3c00087) find
399. Zhiling Zheng, Ali H. Alawadhi, Saumil Chheda, S. Ephraim Neumann, Nakul Rampal, Shengchao Liu, Ha L. Nguyen, Yen-hsu Lin, Zichao Rong, J. Ilja Siepmann, Laura Gagliardi, Anima Anandkumar, Christian Borgs, Jennifer T. Chayes, and Omar M. Yaghi. Shaping the Water-Harvesting Behavior of Metal-Organic… — [doi:10.1021/jacs.3c12086](https://doi.org/10.1021/jacs.3c12086) find
400. Zhiling Zheng, Oufan Zhang, Christian Borgs, Jennifer T. Chayes, and Omar M. Yaghi. ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis. Journal of the American Chemical Society, 145 (Copyright © 2024 American Chemical Society (ACS). All Rights Reserved.; Copyright © 2024… find
401. Zikai Xie, Xenophon Evangelopoulos, Ömer H. Omar, Alessandro Troisi, Andrew I. Cooper, and Linjiang Chen. Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules. Chemical Science, 15(2):500–510, January 2024. ISSN 2041-6539. doi:10.1039/D3SC04610A. URL https… — [doi:10.1039/d3sc04610a](https://doi.org/10.1039/d3sc04610a) find
402. Zhiling Zheng, Zichao Rong, Nakul Rampal, Christian Borgs, Jennifer T. Chayes, and Omar M. Yaghi. A GPT-4 Reticular Chemist for Guiding MOF Discovery. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 62 (46), November 2023. ISSN 1433-7851. doi:10.1002/anie.202311983. — [doi:10.1002/anie.202311983](https://doi.org/10.1002/anie.202311983) find
403. Jyotirmoy Deb, Lakshi Saikia, Kripa Dristi Dihingia, and G. Narahari Sastry. Chatgpt in the material design: Selected case studies to assess the potential of chatgpt. Journal of Chemical Information and Modeling, 64 (3):799–811, 2024. doi:10.1021/acs.jcim.3c01702. URL https://doi.org/10.1021/acs.jci… — [doi:10.1021/acs.jcim.3c01702](https://doi.org/10.1021/acs.jcim.3c01702) ↗
404. Benjamin S Bloom. Taxonomy of Educational Objectives: The Classification of Educational Goals; Handbook. Cognitive Domain. McKay, 1968. find
405. Benjamin Samuel Bloom. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman, 2010. find
406. Connor W. Coley, Dale A. Thomas, Justin A. M. Lummiss, Jonathan N. Jaworski, Christopher P. Breen, Victor Schultz, Travis Hart, Joshua S. Fishman, Luke Rogers, Hanyu Gao, Robert W. Hicklin, Pieter P. Plehiers, Joshua Byington, John S. Piotti, William H. Green, A. John Hart, Timothy F. Jamison, and K… — [doi:10.1126/science.aax1566](https://doi.org/10.1126/science.aax1566) find
407. Piotr S. Gromski, Jarosław M. Granda, and Leroy Cronin. Universal Chemical Synthesis and Discovery with ‘The Chemputer’. Trends in Chemistry, 2(1):4–12, January 2020. ISSN 25895974. doi:10.1016/j.trechm.2019.07.004. URL https://linkinghub.elsevier.com/retrieve/pii/S2589597419301868. — [doi:10.1016/j.trechm.2019.07.004](https://doi.org/10.1016/j.trechm.2019.07.004) ↗
408. Francesca Grisoni, Berend J. H. Huisman, Alexander L. Button, Michael Moret, Kenneth Atz, Daniel Merk, and Gisbert Schneider. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Science Advances, 7(24):eabg3338, June 2021. doi:10.1126/sciadv.abg3338. URL https… — [doi:10.1126/sciadv.abg3338](https://doi.org/10.1126/sciadv.abg3338) find
409. Brian Goldman, Steven Kearnes, Trevor Kramer, Patrick Riley, and W. Patrick Walters. Defining Levels of Automated Chemical Design. Journal of Medicinal Chemistry, 65(10):7073–7087, May 2022. ISSN 0022-2623, 1520-4804. doi:10.1021/acs.jmedchem.2c00334. URL https://pubs.acs.org/doi/10.1021/acs. jmedch… — [doi:10.1021/acs.jmedchem.2c00334](https://doi.org/10.1021/acs.jmedchem.2c00334) ↗
410. Gisbert Schneider. Automating drug discovery. Nature Reviews Drug Discovery, 17(2):97–113, February 2018. ISSN 1474-1784. doi:10.1038/nrd.2017.232. URL https://doi.org/10.1038/nrd.2017.232. — [doi:10.1038/nrd.2017.232](https://doi.org/10.1038/nrd.2017.232) ↗
411. Jon Paul Janet, Lewis Mervin, and Ola Engkvist. Artificial intelligence in molecular de novo design: Integration with experiment. Current Opinion in Structural Biology, 80:102575, June 2023. ISSN 0959-440X. doi:10.1016/j.sbi.2023.102575. URL https://www.sciencedirect.com/science/article/pii/ S095944… — [doi:10.1016/j.sbi.2023.102575](https://doi.org/10.1016/j.sbi.2023.102575) ↗
412. Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angewandte Chemie International Edition, 59(51):22858–22893, 2020. ISSN 1521-3773. doi:10.1002/anie.201909987. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/anie. 201909987. _… — [doi:10.1002/anie.201909987](https://doi.org/10.1002/anie.201909987) ↗
413. Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen. Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angewandte Chemie International Edition, 59(52):23414–23436, 2020. ISSN 1521-3773. doi:10.1002/anie.201909989. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/anie. 201909989. _… — [doi:10.1002/anie.201909989](https://doi.org/10.1002/anie.201909989) ↗
414. Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean-Louis Reymond, and Ola Engkvist. Artificial intelligence and automation in computer aided synthesis planning. Reaction Chemistry & Engineering, 6(1): 27–51, January 2021. ISSN 2058-9883. doi:10.1039/D0RE00340A. URL https://pubs.rsc.org/… — [doi:10.1039/d0re00340a](https://doi.org/10.1039/d0re00340a) ↗
415. Yuning Shen, Julia E. Borowski, Melissa A. Hardy, Richmond Sarpong, Abigail G. Doyle, and Tim Cernak. Automation and computer-assisted planning for chemical synthesis. Nature Reviews Methods Primers, 1(1): 23, March 2021. ISSN 2662-8449. doi:10.1038/s43586-021-00022-5. URL https://www.nature.com/ ar… — [doi:10.1038/s43586-021-00022-5](https://doi.org/10.1038/s43586-021-00022-5) ↗
416. Yuxiao Liu, Lingyu Sun, Hui Zhang, Luoran Shang, and Yuanjin Zhao. Microfluidics for Drug Development: From Synthesis to Evaluation. Chemical Reviews, 121(13):7468–7529, July 2021. ISSN 0009-2665. doi:10.1021/acs.chemrev.0c01289. URL https://doi.org/10.1021/acs.chemrev.0c01289. Publisher: American C… — [doi:10.1021/acs.chemrev.0c01289](https://doi.org/10.1021/acs.chemrev.0c01289) ↗
417. Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, and Florian Shkurti. ORGANA: A robotic assistant for automated chemistry experimentation and characterization. arXiv [cs.RO], January 2024. UR… — [arXiv:2401.06949](https://arxiv.org/abs/2401.06949) find
418. Tomáš Šalamon. Design of Agent-based Models: Developing Computer Simulations for a Better Understanding of Social Processes. Tomáš Bruckner, Repin, CZE, 2011. ISBN 9788090466111. URL https://play.google.com/store/books/details?id=2rCdKnltaH8C. ↗
419. Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang,… — The Rise and Potential of Large Language Model Based Agents: A Survey
420. Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, and Marinka Zitnik. Empowering biomedical discovery with AI agents. arXiv [cs.AI], April 2024. URL http://arxiv.org/abs/2404.02831. ↗
421. Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Jirong Wen. A survey on large language model based autonomous agents. Front. Comput. Sci., 18(6), December 2024. ISSN 2095-2228,2095-2236. doi:10.1007…
422. Lilian Weng. 2023-06-23-agent/, June 2023. URL https://lilianweng.github.io/posts/2023-06-23-agent/. Accessed: 2024-1-22. ↗
423. Theodore R Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. Cognitive architectures for language agents. arXiv [cs.AI], September 2023. URL http://arxiv.org/abs/2309.02427. ↗
424. Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Shuangzhi Wu, Peihao Wu, Lu Lu, Zejun Ma, and Zhoujun Li. Enhancing large language model with self-controlled memory framework. arXiv [cs.CL], April 2023. URL http://arxiv.org/abs/2304.13343. — [arXiv:2304.13343](https://arxiv.org/abs/2304.13343) ↗
425. Yi Zhang, Zhongyang Yu, Wanqi Jiang, Yufeng Shen, and Jin Li. Long-term memory for large language models through topic-based vector database. In 2023 International Conference on Asian Language Processing (IALP). IEEE, November 2023. doi:10.1109/ialp61005.2023.10337079. URL https://ieeexplore.ieee. o… — [doi:10.1109/ialp61005.2023.10337079](https://doi.org/10.1109/ialp61005.2023.10337079) ↗
426. Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, Yu Qiao, Zhaoxiang Zhang, and Jifeng Dai. Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory. a… find
427. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. MemoryBank: Enhancing large language models with long-term memory. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.10250. ↗
428. Yikun Han, Chunjiang Liu, and Pengfei Wang. A comprehensive survey on vector database: Storage and retrieval technique, challenge. arXiv [cs.DB], October 2023. URL http://arxiv.org/abs/2310.11703. — [arXiv:2310.11703](https://arxiv.org/abs/2310.11703) ↗
429. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. ExpeL: LLM agents are experiential learners. arXiv [cs.LG], August 2023. URL http://arxiv.org/abs/2308.10144. ↗
430. ANN-benchmarks. https://ann-benchmarks.com/. URL https://ann-benchmarks.com/. Accessed: 2024-2-1. ↗
431. Kostas Hatalis, Despina Christou, Joshua Myers, Steven Jones, Keith Lambert, Adam Amos-Binks, Zohreh Dannenhauer, and Dustin Dannenhauer. Memory matters: The need to improve Long-Term memory in LLM-Agents. AAAI-SS, 2(1):277–280, 2023. ISSN 2994-4317, 2994-4317. doi:10.1609/aaaiss.v2i1.27688. URL htt… — [doi:10.1609/aaaiss.v2i1.27688](https://doi.org/10.1609/aaaiss.v2i1.27688) find
432. Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. arXiv [cs.HC], April 2023. URL http://arxiv.org/abs/2304.03442. ↗
433. S S Raman, V Cohen, E Rosen, and I Idrees. Planning with large language models via corrective re-prompting. Foundation Models, November 2022. URL https://openreview.net/pdf?id=cMDMRBe1TKs. ↗
434. Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. Chain-of-verification reduces hallucination in large language models. arXiv [cs.CL], September 2023. URL http://arxiv.org/abs/2309.11495. ↗
435. Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. Inner monologue: Embodied reasoning through planning with lan… find
436. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. arXiv [cs.CL], pages 22199–22213, May 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/ 8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf. ↗
437. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, E Chi, F Xia, Quoc Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. Neural Inf Process Syst, abs/2201.11903, January 2022. URL http://arxiv.org/abs/2201.11903. ↗
438. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv [cs.CL], March 2022. URL http://arxiv.org/abs/2203.11171. ↗
439. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. arXiv [cs.CL], October 2022. URL http://arxiv.org/abs/2210.03629. ↗
440. Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/ 2305.14992. ↗
441. Hao Liu, Carmelo Sferrazza, and Pieter Abbeel. Chain of hindsight aligns language models with feedback. arXiv [cs.LG], February 2023. URL http://arxiv.org/abs/2302.02676. ↗
442. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.10601. ↗
443. Jikun Kang, Romain Laroche, Xingdi Yuan, Adam Trischler, Xue Liu, and Jie Fu. Think before you act: Decision transformers with working memory. arXiv [cs.LG], May 2023. URL http://arxiv.org/abs/2305.16338. — [arXiv:2305.16338](https://arxiv.org/abs/2305.16338) ↗
444. Cheng Qian, Shihao Liang, Yujia Qin, Yining Ye, Xin Cong, Yankai Lin, Yesai Wu, Zhiyuan Liu, and Maosong Sun. Investigate-consolidate-exploit: A general strategy for inter-task agent self-evolution. arXiv [cs.CL], January 2024. URL http://arxiv.org/abs/2401.13996. — [arXiv:2401.13996](https://arxiv.org/abs/2401.13996) ↗
445. Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Seba… — PaLM 2 Technical Report
446. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems. arXiv [cs.LG], October 2021. URL http://arxiv.org/abs/2110.1416… ↗
447. Zhanming Jie, Jierui Li, and Wei Lu. Learning to reason deductively: Math word problem solving as complex relation extraction. arXiv [cs.CL], March 2022. URL http://arxiv.org/abs/2203.10316. ↗
448. Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, and Ee-Peng Lim. MWPToolkit: An open-source framework for deep learning-based math word problem solvers. arXiv [cs.CL], September 2021. URL http://arxiv.org/abs/2109.00799. ↗
449. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. Graph of thoughts: Solving elaborate problems with large language models. arXiv [cs.CL], August 2023. URL http://ar… find
450. Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin. Algorithm of thoughts: Enhancing exploration of ideas in large language models. arXiv [cs.CL], August 2023. URL http://arxiv.org/abs/ 2308.10379. ↗
451. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, and Shuming Shi. Encouraging divergent thinking in large language models through multi-agent debate. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.19118. ↗
452. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. arXiv [cs.CL], May 2023. URL http://arxiv.org/ abs/2305.14325. ↗
453. Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shan Zhang, Jie Fu, and Zhiyuan Liu. ChatEval: Towards better LLM-based evaluators through multi-agent debate. ArXiv, abs/2308.07201, August 2023. ISSN 2331-8422. doi:10.48550/arXiv.2308.07201. URL http://dx.doi.org/10.48550/arXiv.2308.0720… ↗
454. Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. LLM-planner: Few-shot grounded planning for embodied agents with large language models. arXiv [cs.AI], December 2022. URL http://arxiv.org/abs/2212.04088. ↗
455. Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. LLM+P: Empowering large language models with optimal planning proficiency. arXiv [cs.AI], April 2023. URL http://arxiv.org/abs/2304.11477. ↗
456. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-… find
457. Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Tao Gui, Qi Zhang, and Xuanjing Huang. Self-polish: Enhance reasoning in large language models via problem refinement. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.14497. ↗
458. Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv [cs.AI], February 2023. URL http://arxiv.org/abs/2302.01560. ↗
459. Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. Toxicity in ChatGPT: Analyzing persona-assigned language models. arXiv [cs.CL], April 2023. URL http://arxiv.org/abs/2304.05335. ↗
460. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jurgen Schmidhuber. MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv [cs.AI],…
461. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: Communicative agents for “mind” exploration of large language model society. arXiv [cs.AI], March 2023. URL http://arxiv.org/abs/2303.17760. ↗
462. Shi Jinxin, Zhao Jiabao, Wang Yilei, Wu Xingjiao, Li Jiawen, and He Liang. CGMI: Configurable general multi-agent interaction framework. arXiv [cs.AI], August 2023. URL http://arxiv.org/abs/2308.12503. — [arXiv:2308.12503](https://arxiv.org/abs/2308.12503) ↗
463. Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development. arXiv [cs.SE], July 2023. URL http://arxiv.org/abs/2307.07924. ↗
464. Yunfan Shao, Linyang Li, Junqi Dai, and Xipeng Qiu. Character-LLM: A trainable agent for role-playing. arXiv [cs.CL], October 2023. URL http://arxiv.org/abs/2310.10158. ↗
465. Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, and Minlie Huang. CritiqueLLM: Scaling LLM-as-critic for effective and explainable evaluation of large language model generation. arXiv [cs.CL], November 2023. URL http…
466. Lei Wang, Jingsen Zhang, Hao Yang, Zhiyuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, and Ji-Rong Wen. User behavior simulation with large language model based agents. arXiv [cs.IR], June 2023. URL http://arxiv.org/abs/2306.02552. ↗
467. Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate. Out of one, many: Using language models to simulate human samples. Polit. Anal., 31(3):337–351, July 2023. ISSN 1047-1987, 1476-4989. doi:10.1017/pan.2023.2. URL https://www.cambridge.org/core/journal… ↗
468. Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. Segment anything. arXiv [cs.CV], April 2023. URL http://arxiv.org/abs/2304.02643. ↗
469. AI Open. GPT-4V(ision) system card, 2023. URL https://cdn.openai.com/papers/GPTV_System_Card. pdf. ↗
470. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. arXiv [cs.CV], April 2023. URL http://arxiv.org/abs/2304.08485. ↗
471. Yang Zhao, Zhijie Lin, Daquan Zhou, Zilong Huang, Jiashi Feng, and Bingyi Kang. BuboGPT: Enabling visual grounding in multi-modal LLMs. arXiv [cs.CV], July 2023. URL http://arxiv.org/abs/2307.08581. — [arXiv:2307.08581](https://arxiv.org/abs/2307.08581) ↗
472. Chenyang Lyu, Minghao Wu, Longyue Wang, Xinting Huang, Bingshuai Liu, Zefeng Du, Shuming Shi, and Zhaopeng Tu. Macaw-LLM: Multi-modal language modeling with image, audio, video, and text integration. arXiv [cs.CL], June 2023. URL http://arxiv.org/abs/2306.09093. ↗
473. Chenyu Wang, Weixin Luo, Qianyu Chen, Haonan Mai, Jindi Guo, Sixun Dong, Xiaohua, Xuan, Zhengxin Li, Lin Ma, and Shenghua Gao. Tool-LMM: A large multi-modal model for tool agent learning. arXiv [cs.CV], January 2024. URL http://arxiv.org/abs/2401.10727. — [arXiv:2401.10727](https://arxiv.org/abs/2401.10727) ↗
474. Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, and Mike Zheng Shou. AssistGPT: A general multi-modal assistant that can plan, execute, inspect, and learn. arXiv [cs.CV], June 2023. URL http://arxiv.org/abs/2306.08640. — [arXiv:2306.08640](https://arxiv.org/abs/2306.08640) ↗
475. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv [cs.AI], May 2023. URL http://arxiv.org/abs/2305.16291. ↗
476. Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil… — Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
477. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhai… — Evaluating Large Language Models Trained on Code
478. Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Z… — Tool Learning with Foundation Models
479. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. WebGPT: Browser-assisted qu… — WebGPT: Browser-assisted question-answering with human feedback
480. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv [cs.CL], February 2023. URL http://arxiv.org/abs/2302.04761. ↗
481. Aaron Parisi, Yao Zhao, and Noah Fiedel. TALM: Tool augmented language models. arXiv [cs.CL], May 2022. URL http://arxiv.org/abs/2205.12255. ↗
482. Cheng Qian, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. Toolink: Linking toolkit creation and using through chain-of-solving on open-source model. arXiv [cs.CL], October 2023. URL http://arxiv.org/abs/ 2310.05155. ↗
483. Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, and Moshe Tenenholtz. MRKL systems: A modular, neuro-symbolic architectu… — [arXiv:2205.00445](https://arxiv.org/abs/2205.00445) find
484. Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Wayne Xin Zhao, and Ji-Rong Wen. ChatCoT: Tool-augmented chain-of-thought reasoning on chat-based large language models. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.14323. ↗
485. Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou. Large language models as tool makers. arXiv [cs.LG], May 2023. URL http://arxiv.org/abs/2305.17126. ↗
486. Cheng Qian, Chi Han, Yi R Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. CREATOR: Disentangling abstract and concrete reasonings of large language models through tool creation. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.14318. — [arXiv:2305.14318](https://arxiv.org/abs/2305.14318) ↗
487. Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi R Fung, Hao Peng, and Heng Ji. CRAFT: Customizing LLMs by creating and retrieving from specialized toolsets. arXiv [cs.CL], September 2023. URL http://arxiv.org/ abs/2309.17428. — [arXiv:2309.17428](https://arxiv.org/abs/2309.17428) ↗
488. Hilmy Abiyyu A. Flaticon. https://www.flaticon.com/authors/hilmy-abiyyu-a. Accessed: 2024-5-1. ↗
489. Laisa Islam Ani. Flaticon. https://www.flaticon.com/authors/laisa-islam-ani. Accessed: 2024-5-1. ↗
490. Freepik. Flaticon. https://www.flaticon.com/authors/freepik. Accessed: 2024-5-1. ↗
491. Kiranshastry. Flaticon. https://www.flaticon.com/authors/kiranshastry. Accessed: 2024-5-1. ↗
492. Michael D Skarlinski, Sam Cox, Jon M Laurent, James D Braza, Michaela Hinks, Michael J Hammerling, Manvitha Ponnapati, Samuel G Rodriques, and Andrew D White. Language agents achieve superhuman synthesis of scientific knowledge. arXiv [cs.CL], September 2024. URL http://arxiv.org/abs/2409.13740. ↗
493. Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, and Janosh Riebesell. LLaMP: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation. arXiv [cs.CL], January 2024. URL http://arxiv.org/abs/2401.17244. ↗
494. Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B Tenenbaum, Daniela Rus, Chuang Gan, and Wojciech Matusik. LLM and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery. arXiv [cs.LG], May 2024. URL http://arxiv.org/abs/2405.09783. ↗
495. Yuanhao Qu, Kaixuan Huang, Henry Cousins, William A Johnson, Di Yin, Mihir Shah, Denny Zhou, Russ Altman, Mengdi Wang, and Le Cong. CRISPR-GPT: An LLM agent for automated design of gene-editing experiments. bioRxiv, April 2024. doi:10.1101/2024.04.25.591003. URL http://dx.doi.org/10.48550/ arXiv.240… — [doi:10.1101/2024.04.25.591003](https://doi.org/10.1101/2024.04.25.591003) find
496. Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng, Jianrong Lu, Shuyi Guo, Jinglei Zhu, Mianchen Zhang, Miantong Zhang, and Haohan Wang. Toward a team of AI-made scientists for scientific discovery from gene expression data. arXiv [q-bio.GN], February 2024. URL http://arxiv.org/abs/2402.12391. — [arXiv:2402.12391](https://arxiv.org/abs/2402.12391) ↗
497. Henry W Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V Olarte, Udishnu Sanyal, Conrad Johnston, Hongbin Liu, Heng Ji, and Sutanay Choudhury. ChemReasoner: Heuristic search over a large language model’s knowledge space using quantum-chemical feedback. arXiv [physics.chem-ph], February 2024. URL… find
498. Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, and Aixin Sun. SciAgent: Tool-augmented language models for scientific reasoning. arXiv [cs.CL], February 2024. URL http://arxiv.org/abs/2402.11451. ↗
499. Yijia Shao, Yucheng Jiang, Theodore A Kanell, Peter Xu, Omar Khattab, and Monica S Lam. Assisting in writing wikipedia-like articles from scratch with large language models. arXiv [cs.CL], February 2024. URL http://arxiv.org/abs/2402.14207. ↗
500. Christoph Völker, Tehseen Rug, Kevin Maik Jablonka, and Sabine Kruschwitz. LLMs can design sustainable concrete – a systematic benchmark. February 2024. URL https://www.researchsquare.com/article/ rs-3913272/v1. ↗
501. A Ghafarollahi and M J Buehler. ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning. arXiv [cond-mat.soft], January 2024. URL http://arxiv.org/abs/2402.04268. — [arXiv:2402.04268](https://arxiv.org/abs/2402.04268) ↗
502. Jakub Lála, Odhran O’Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, and Andrew D White. PaperQA: Retrieval-augmented generative agent for scientific research. arXiv [cs.CL], December 2023. URL http://arxiv.org/abs/2312.07559. ↗
503. Sam Cox, Michael Hammerling, Jakub Lála, Jon Laurent, Sam Rodriques, Matt Rubashkin, Andrew White. WikiCrow: Automating synthesis of human scientific knowledge. https://www.futurehouse.org/wikicrow, 2023. URL https://www.futurehouse.org/wikicrow. Accessed: 2024-2-15. ↗
504. Mehrad Ansari and Seyed Mohamad Moosavi. Agent-based learning of materials datasets from scientific literature. arXiv [cs.AI], December 2023. URL http://arxiv.org/abs/2312.11690. — [arXiv:2312.11690](https://arxiv.org/abs/2312.11690) ↗
505. Michael H Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K Sastry, Matthew T Dearing, Ross J Harder, Rama K Vasudevan, and Mathew J Cherukara. Opportunities for retrieval and tool augmented large language models in scientific facilities. arXiv [cs.CE], December 2023. URL http://arxiv.org/abs… — [arXiv:2312.01291](https://arxiv.org/abs/2312.01291) ↗
506. Yiren Liu, Si Chen, Haocong Cheng, Mengxia Yu, Xiao Ran, Andrew Mo, Yiliu Tang, and Yun Huang. CoQuest: Exploring research question co-creation with an LLM-based agent. arXiv [cs.HC], October 2023. URL http://arxiv.org/abs/2310.06155. — [arXiv:2310.06155](https://arxiv.org/abs/2310.06155) ↗
507. Odhran O’Donoghue, Aleksandar Shtedritski, John Ginger, Ralph Abboud, Ali Essa Ghareeb, Justin Booth, and Samuel G Rodriques. BioPlanner: Automatic evaluation of LLMs on protocol planning in biology. arXiv [cs.CL], October 2023. URL http://arxiv.org/abs/2310.10632. ↗
508. Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan, Teodoro Laino, and Jannis Born. Language models in molecular discovery. arXiv [physics.chem-ph], September 2023. URL http://arxiv.org/abs/2309.16235. — [arXiv:2309.16235](https://arxiv.org/abs/2309.16235) ↗
509. Yeonghun Kang and Jihan Kim. ChatMOF: An autonomous AI system for predicting and generating metal-organic frameworks. arXiv [cs.CL], July 2023. URL http://arxiv.org/abs/2308.01423. — [arXiv:2308.01423](https://arxiv.org/abs/2308.01423) ↗
510. Beatriz Mouriño, Elias Moubarak, Joren Van Herck, Sauradeep Majumdar, and Xiaoqi Zhang. i-digest: v1.0, 2023. URL https://zenodo.org/record/8080962. ↗
511. Bojana Rankovic, Andres M Bran, and Philippe Schwaller. BOLLaMa: BOLLaMA interface working with CHAOS, 2023. URL https://zenodo.org/record/8096827. ↗
512. Sabine Kruschwitz, Christoph Völker, and Ghezal Ahmad Zia. Text2Concrete, 2023. URL https://zenodo.org/record/8091195. ↗
513. Mayk Caldas Ramos, Sam Cox, and Andrew White. MAPI_LLM: MAPI_LLM first release, 2023. URL https://zenodo.org/record/8097336. ↗
514. Mayk Caldas Ramos, Shane S Michtavy, Marc D Porosoff, and Andrew D White. Bayesian optimization of catalysts with in-context learning. arXiv [physics.chem-ph], April 2023. URL http://arxiv.org/abs/2304. 05341. ↗
515. Glen M Hocky and Andrew D White. Natural language processing models that automate programming will transform chemistry research and teaching. Digit. Discov., 1(2):79–83, April 2022. ISSN 2635-098X. doi:10.1039/d1dd00009h. URL https://pubs.rsc.org/en/content/articlelanding/2022/ dd/d1dd00009h. — [doi:10.1039/d1dd00009h](https://doi.org/10.1039/d1dd00009h) ↗
516. Andrew D White, Glen M Hocky, Heta A Gandhi, Mehrad Ansari, Sam Cox, Geemi P Wellawatte, Subarna Sasmal, Ziyue Yang, Kangxin Liu, Yuvraj Singh, and Willmor J Peña Ccoa. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery, 2(2):368–376, April 2023. ISSN 26… — [doi:10.1039/d2dd00087c](https://doi.org/10.1039/d2dd00087c) find
517. Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, and Geoff Pleiss. A sober look at LLMs for material discovery: Are they actually good for bayesian optimization over molecules? arXiv [cs.LG], February 2024. URL http://arxiv.org/abs/2402.05015. — [arXiv:2402.05015](https://arxiv.org/abs/2402.05015) ↗
518. Bojana Ranković and Philippe Schwaller. BoChemian: Large language model embeddings for bayesian optimization of chemical reactions. December 2023. URL https://openreview.net/pdf?id=A1RVn1m3J3. ↗
519. K Jablonka, Qianxiang Ai, Alexander H Al-Feghali, S Badhwar, Joshua D Bocarsly Andres Bran, S Bringuier, L Brinson, K Choudhary, Defne Circi, Sam Cox, W D Jong, Matthew L Evans, Nicolas Gastellu, Jerome Genzling, M Gil, Ankur Gupta, Zhi Hong, A Imran, S Kruschwitz, A Labarre, Jakub L’ala, Tao Liu, S… — [doi:10.1039/d3dd00113j](https://doi.org/10.1039/d3dd00113j) find
520. Bojana Ranković, Ryan-Rhys Griffiths, Henry B Moss, and Philippe Schwaller. Bayesian optimisation for additive screening and yield improvements in chemical reactions – beyond one-hot encoding. Chem-Rxiv, June 2023. doi:10.26434/chemrxiv-2022-nll2j-v3. URL https://chemrxiv.org/engage/chemrxiv/ artic… — [doi:10.26434/chemrxiv-2022-nll2j-v3](https://doi.org/10.26434/chemrxiv-2022-nll2j-v3) ↗
521. Benjamin Weiser, Jerome Genzling, Nicolas Gastellu, Sylvester Zhang, Tao Liu, Alexander Al-Feghali, Nicolas Moitessier, Anne Labarre, and Steven Ma. LLM-Guided-GA: LLM-Guided-GA digital discovery release, 2023. URL https://zenodo.org/record/8125541. ↗
522. Defne Circi and Shruti Badhwar. InsightGraph: InsightGraph, 2023. URL https://zenodo.org/record/ 8092575. ↗
523. Marwa Zaabi, Walid Hariri, and Nadia Smaoui. A review study of ChatGPT applications in education. In 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA), pages 1–5. IEEE, September 2023. doi:10.1109/inista59065.2023.10310439. URL http://dx.doi.org/10.1109/ I… — [doi:10.1109/inista59065.2023.10310439](https://doi.org/10.1109/inista59065.2023.10310439) find
524. Enkelejda Kasneci, Kathrin Sessler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, Stephan Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael Sailer, Albrecht Schmidt, T… — [doi:10.1016/j.lindif.2023.102274](https://doi.org/10.1016/j.lindif.2023.102274) find
525. Arto Hellas, Juho Leinonen, Sami Sarsa, Charles Koutcheme, Lilja Kujanpaa, and Juha Sorva. Exploring the responses of large language models to beginner programmers’ help requests. arXiv [cs.CY], June 2023. URL http://arxiv.org/abs/2306.05715. ↗
526. Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie Zhou, Liang He, and Xipeng Qiu. EduChat: A large-scale language model-based chatbot system for intelligent education. arXiv [cs.CL], August 2023. URL…
527. Jie Tian, Jixin Hou, Zihao Wu, Peng Shu, Zhengliang Liu, Yujie Xiang, Beikang Gu, Nicholas Filla, Yiwei Li, Ning Liu, Xianyan Chen, Keke Tang, Tianming Liu, and Xianqiao Wang. Assessing large language models in mechanical engineering education: A study on mechanics-focused conceptual understanding.… — [arXiv:2401.12983](https://arxiv.org/abs/2401.12983) find
528. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-Tau Yih, Tim Rocktaschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv [cs.CL], May 2020. URL http://arxiv.or… ↗
529. Kexin Chen, Junyou Li, Kunyi Wang, Yuyang Du, Jiahui Yu, Jiamin Lu, Lanqing Li, Jiezhong Qiu, Jianzhang Pan, Yi Huang, Qun Fang, Pheng Ann Heng, and Guangyong Chen. Chemist-X: Large language model-empowered agent for reaction condition recommendation in chemical synthesis. arXiv [cs.IR], November 20… — [arXiv:2311.10776](https://arxiv.org/abs/2311.10776) find
530. John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent Frappier, Dana M Lord, Christopher Ng-Thow-Hing, Erik R Van Vlack, Shan Tie, Vincent Xue, Sarah C Cowles, Alan Leung, João V Rodrigues, Claudio L Morales-Perez, Alex M Ayoub, Robin Green, Katherine Puentes,… — [doi:10.1038/s41586-023-06728-8](https://doi.org/10.1038/s41586-023-06728-8) find
531. Kevin E Wu, Kevin K Yang, Rianne van den Berg, Sarah Alamdari, James Y Zou, Alex X Lu, and Ava P Amini. Protein structure generation via folding diffusion. Nat. Commun., 15(1):1059, February 2024. ISSN 2041-1723. doi:10.1038/s41467-024-45051-2. URL http://dx.doi.org/10.1038/s41467-024-45051-2. — [doi:10.1038/s41467-024-45051-2](https://doi.org/10.1038/s41467-024-45051-2) ↗
532. Xiangru Tang, Qiao Jin, Kunlun Zhu, Tongxin Yuan, Yichi Zhang, Wangchunshu Zhou, Meng Qu, Yilun Zhao, Jian Tang, Zhuosheng Zhang, Arman Cohan, Zhiyong Lu, and Mark Gerstein. Prioritizing safeguarding over autonomy: Risks of LLM agents for science. arXiv [cs.CY], February 2024. URL http://arxiv.org/a… ↗
533. Yixiang Ruan, Chenyin Lu, Ning Xu, Jian Zhang, Jun Xuan, Jianzhang Pan, Qun Fang, Hanyu Gao, Xiaodong Shen, Ning Ye, Qiang Zhang, and Yiming Mo. Accelerated end-to-end chemical synthesis development with large language models. ChemRxiv, May 2024. doi:10.26434/chemrxiv-2024-6wmg4. URL https://chemrx… — [doi:10.26434/chemrxiv-2024-6wmg4](https://doi.org/10.26434/chemrxiv-2024-6wmg4) find
534. Zhiling Zheng, Oufan Zhang, Christian Borgs, Jennifer T Chayes, and Omar M Yaghi. ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis. J. Am. Chem. Soc., 145(Copyright © 2024 American Chemical Society (ACS). All Rights Reserved.; Copyright © 2024 U.S. National Library of… find
535. Zhiling Zheng, Zhiguo He, Omar Khattab, Nakul Rampal, Matei A Zaharia, Christian Borgs, Jennifer T Chayes, and Omar M Yaghi. Image and data mining in reticular chemistry powered by GPT-4V. Digit. Discov., 3(3): 491–501, 2024. ISSN 2635-098X. doi:10.1039/d3dd00239j. URL https://pubs.rsc.org/en/conten… — [doi:10.1039/d3dd00239j](https://doi.org/10.1039/d3dd00239j) ↗
536. Zhiling Zheng, Oufan Zhang, Ha L Nguyen, Nakul Rampal, Ali H Alawadhi, Zichao Rong, Teresa Head-Gordon, Christian Borgs, Jennifer T Chayes, and Omar M Yaghi. ChatGPT research group for optimizing the crystallinity of MOFs and COFs. ACS Cent. Sci., 9(11):2161–2170, November 2023. ISSN 2374-7943,2374-… — [doi:10.1021/acscentsci.3c01087](https://doi.org/10.1021/acscentsci.3c01087) find
537. Nathalia Nascimento, Paulo Alencar, and Donald Cowan. Self-adaptive large language model (LLM)-based multiagent systems. In 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pages 104–109. IEEE, September 2023. ISBN 9798350337464. doi:10.1109/… — [doi:10.1109/acsos-c58168.2023.00048](https://doi.org/10.1109/acsos-c58168.2023.00048) find
538. Sarfaraz K Niazi and Zamara Mariam. Recent advances in Machine-Learning-Based chemoinformatics: A comprehensive review. Int. J. Mol. Sci., 24(14), July 2023. ISSN 1422-0067. doi:10.3390/ijms241411488. URL http://dx.doi.org/10.3390/ijms241411488. — [doi:10.3390/ijms241411488](https://doi.org/10.3390/ijms241411488) ↗
539. Andrew D McNaughton, Gautham Ramalaxmi, Agustin Kruel, Carter R Knutson, Rohith A Varikoti, and Neeraj Kumar. CACTUS: Chemistry agent connecting tool-usage to science. arXiv [cs.CL], May 2024. URL http://arxiv.org/abs/2405.00972. — [arXiv:2405.00972](https://arxiv.org/abs/2405.00972) ↗
540. Yeonghun Kang, Hyunsoo Park, Berend Smit, and Jihan Kim. A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks. Nature Machine Intelligence, 5(3):309–318, March 2023. ISSN 2522-5839, 2522-5839. doi:10.1038/s42256-023-00628-2. URL https://www.nature.com/ a… — [doi:10.1038/s42256-023-00628-2](https://doi.org/10.1038/s42256-023-00628-2) ↗
541. Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit… — [doi:10.1038/s41524-023-01028-1](https://doi.org/10.1038/s41524-023-01028-1) find
542. rxn4chemistry: Python wrapper for the IBM RXN for chemistry API. rxn4chemistry/rxn4chemistry. URL https://github.com/ ↗
543. Piotr Gaiński, Lukasz Maziarka, Tomasz Danel, and Stanislaw Jastrzebski. HuggingMolecules: An Open-Source library for Transformer-Based molecular property prediction (student abstract). AAAI, 36(11):12949–12950, June 2022. ISSN 2374-3468, 2374-3468. doi:10.1609/aaai.v36i11.21611. URL https://ojs.aa… — [doi:10.1609/aaai.v36i11.21611](https://doi.org/10.1609/aaai.v36i11.21611) ↗
544. Greg Landrum. Rdkit documentation. Release, 1(1-79):4, 2013. ISSN 1047-935X, 1533-3752. URL https://media.readthedocs.org/pdf/rdkit/latest/rdkit.pdf. ↗
545. Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh T N Nguyen, Lauren T May, Geoffrey I Webb, and Shirui Pan. Large language models for scientific synthesis, inference and explanation. arXiv [cs.AI], October 2023. URL http://arxiv.org/abs/2310.07984. — [arXiv:2310.07984](https://arxiv.org/abs/2310.07984) ↗
546. Qingyun Wang, Doug Downey, Heng Ji, and Tom Hope. SciMON: Scientific inspiration machines optimized for novelty. arXiv [cs.CL], May 2023. URL http://arxiv.org/abs/2305.14259. ↗
547. Xuemei Gu and Mario Krenn. Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models. arXiv [cs.AI], May 2024. URL http://arxiv.org/abs/2405. 17044. ↗
548. Henry W Sprueill, Carl Edwards, Mariefel V Olarte, Udishnu Sanyal, Heng Ji, and Sutanay Choudhury. Monte carlo thought search: Large language model querying for complex scientific reasoning in catalyst design. arXiv [cs.AI], October 2023. URL http://arxiv.org/abs/2310.14420. ↗
549. Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-Mol: A universal 3D molecular representation learning framework. ChemRxiv, March 2023. doi:10.26434/chemrxiv-2022-jjm0j-v4. URL https://chemrxiv.org/engage/chemrxiv/ article-details/6402990… — [doi:10.26434/chemrxiv-2022-jjm0j-v4](https://doi.org/10.26434/chemrxiv-2022-jjm0j-v4) ↗
550. Santiago Miret and N M Anoop Krishnan. Are LLMs ready for real-world materials discovery? [cond-mat.mtrl-sci], February 2024. URL http://arxiv.org/abs/2402.05200. arXiv ↗
551. Yuanqi Du, Chenru Duan, Andres Bran, Anna Sotnikova, Yi Qu, Heather Kulik, Antoine Bosselut, Jinjia Xu, and Philippe Schwaller. Large language models are catalyzing chemistry education. Chem-Rxiv, June 2024. doi:10.26434/chemrxiv-2024-h722v. URL https://chemrxiv.org/engage/ api-gateway/chemrxiv/asse… — [doi:10.26434/chemrxiv-2024-h722v](https://doi.org/10.26434/chemrxiv-2024-h722v) ↗
552. Jinlu Zhang, Yin Fang, Xin Shao, Huajun Chen, Ningyu Zhang, and Xiaohui Fan. The future of molecular studies through the lens of large language models. J. Chem. Inf. Model., 64(3):563–566, February 2024. ISSN 1549-9596, 1549-960X. doi:10.1021/acs.jcim.3c01977. URL http://dx.doi.org/10.1021/acs.jcim.… — [doi:10.1021/acs.jcim.3c01977](https://doi.org/10.1021/acs.jcim.3c01977) ↗
553. OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Ja… — GPT-4 Technical Report
554. Wenhao Gao and Connor W Coley. The synthesizability of molecules proposed by generative models. J. Chem. Inf. Model., (12):5714–5723, February 2020. ISSN 1549-9596,1549-960X. doi:10.1021/acs.jcim.0c00174. URL https://pubs.acs.org/doi/10.1021/acs.jcim.0c00174. — [doi:10.1021/acs.jcim.0c00174](https://doi.org/10.1021/acs.jcim.0c00174) ↗
555. Pengfei Liu, Jun Tao, and Zhixiang Ren. Scientific language modeling: A quantitative review of large language models in molecular science. arXiv [cs.LG], February 2024. URL http://arxiv.org/abs/2402.04119. — [arXiv:2402.04119](https://arxiv.org/abs/2402.04119) ↗
556. Philippe Schwaller, Benjamin Hoover, Jean-Louis Reymond, Hendrik Strobelt, and Teodoro Laino. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci. Adv., 7(15):eabe4166, April 2021. ISSN 2375-2548. doi:10.1126/sciadv.abe4166. URL http://dx.doi.org/10.1126/sci… — [doi:10.1126/sciadv.abe4166](https://doi.org/10.1126/sciadv.abe4166) ↗
557. Oliver Schilter, Marvin Alberts, Federico Zipoli, Alain C Vaucher, Philippe Schwaller, and Teodoro Laino. Unveiling the secrets of 1 H-NMR spectroscopy: A novel approach utilizing attention mechanisms. November 2023. URL https://openreview.net/pdf?id=4ilKwquW51. ↗
558. Shiva Aryal, Tuyen Do, Bisesh Heyojoo, Sandeep Chataut, Bichar Dip Shrestha Gurung, Venkataramana Gadhamshetty, and Etienne Gnimpieba. Leveraging multi-AI agents for cross-domain knowledge discovery. arXiv [cs.AI], April 2024. URL http://arxiv.org/abs/2404.08511. — [arXiv:2404.08511](https://arxiv.org/abs/2404.08511) ↗
559. Florian Bohm, Yang Gao, Christian M Meyer, Ori Shapira, Ido Dagan, and Iryna Gurevych. Better rewards yield better summaries: Learning to summarise without references. arXiv [cs.CL], September 2019. URL http://arxiv.org/abs/1909.01214. ↗
560. Sarah Pan, Vladislav Lialin, Sherin Muckatira, and Anna Rumshisky. Let’s reinforce step by step. arXiv [cs.CL], November 2023. URL http://arxiv.org/abs/2311.05821. — [arXiv:2311.05821](https://arxiv.org/abs/2311.05821) ↗
561. Bin Hu, Chenyang Zhao, Pu Zhang, Zihao Zhou, Yuanhang Yang, Zenglin Xu, and Bin Liu. Enabling intelligent interactions between an agent and an LLM: A reinforcement learning approach. arXiv [cs.AI], June 2023. URL http://arxiv.org/abs/2306.03604. — [arXiv:2306.03604](https://arxiv.org/abs/2306.03604) ↗
562. Zelai Xu, Chao Yu, Fei Fang, Yu Wang, and Yi Wu. Language agents with reinforcement learning for strategic play in the werewolf game. arXiv [cs.AI], October 2023. URL http://arxiv.org/abs/2310.18940. — [arXiv:2310.18940](https://arxiv.org/abs/2310.18940) ↗
563. Meredith Ringel Morris, Jascha Sohl-dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, and Shane Legg. Levels of AGI for operationalizing progress on the path to AGI. arXiv [cs.AI], November 2023. URL http://arxiv.org/abs/2311.02462. — [arXiv:2311.02462](https://arxiv.org/abs/2311.02462) ↗
564. Types of artificial intelligence. https://www.ibm.com/think/topics/ artificial-intelligence-types, August 2024. Accessed: 2024-10-9. ↗
565. Pat Langley, John E Laird, and Seth Rogers. Cognitive architectures: Research issues and challenges. Cogn. Syst. Res., 10(2):141–160, June 2009. ISSN 1389-0417,2214-4366. doi:10.1016/j.cogsys.2006.07.004. URL http://dx.doi.org/10.1016/j.cogsys.2006.07.004. — [doi:10.1016/j.cogsys.2006.07.004](https://doi.org/10.1016/j.cogsys.2006.07.004) ↗
566. Ben Goertzel. Artificial general intelligence: Concept, state of the art, and future prospects. J. Artif. Gen. Intell., 5(1):1–48, December 2014. ISSN 1946-0163. doi:10.2478/jagi-2014-0001. URL https://sciendo.com/ pdf/10.2478/jagi-2014-0001. — [doi:10.2478/jagi-2014-0001](https://doi.org/10.2478/jagi-2014-0001) ↗
567. Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J Maddison, and Tatsunori Hashimoto. Identifying the risks of LM agents with an LM-emulated sandbox. arXiv [cs.AI], September 2023. URL http://arxiv.org/abs/2309.15817. ↗
568. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12):1–38, March 2023. ISSN 0360-0300. doi:10.1145/3571730. URL https://doi.org/10.1145/3571730. ↗
569. Zefan Cai, Baobao Chang, and Wenjuan Han. Human-in-the-loop through chain-of-thought. arXiv [cs.CL], June 2023. URL http://arxiv.org/abs/2306.07932. — [arXiv:2306.07932](https://arxiv.org/abs/2306.07932) ↗
570. Hengjia Xiao and Peng Wang. LLM a*: Human in the loop large language models enabled a* search for robotics. arXiv [cs.RO], December 2023. URL http://arxiv.org/abs/2312.01797. — [arXiv:2312.01797](https://arxiv.org/abs/2312.01797) ↗
571. Iddo Drori and Dov Te’eni. Human-in-the-Loop AI reviewing: Feasibility, opportunities, and risks. Journal of the Association for Information Systems, 25(1):98–109, 2024. ISSN 1536-9323. doi:10.17705/1jais.00867. URL https://aisel.aisnet.org/jais/vol25/iss1/7/. — [doi:10.17705/1jais.00867](https://doi.org/10.17705/1jais.00867) ↗
572. Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E. Kumar, Tanjin He, David Milsted, Matthew J. McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, Haegyeom Kim, Anubhav Jain, Christopher J. Bartel, Kristin Persson, Yan Zeng, and Gerbrand Ceder. An autonomous laboratory for the accelerat… — [doi:10.1038/s41586-023-06734-w](https://doi.org/10.1038/s41586-023-06734-w) find
573. Mark Peplow. Robot chemist sparks row with claim it created new materials. Nature, December 2023. doi:10.1038/d41586-023-03956-w. URL https://www.nature.com/articles/d41586-023-03956-w. Bandiera_abtest: a Cg_type: News Publisher: Nature Publishing Group Subject_term: Scientific community, Materials… — [doi:10.1038/d41586-023-03956-w](https://doi.org/10.1038/d41586-023-03956-w) ↗
574. Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Wenyi Wang, Xiangru Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Zongze Xu, an… — Data Interpreter: An LLM Agent for Data Science
575. Danrui Qi and Jiannan Wang. CleanAgent: Automating data standardization with LLM-based agents. arXiv [cs.LG], March 2024. URL http://arxiv.org/abs/2403.08291. — [arXiv:2403.08291](https://arxiv.org/abs/2403.08291) ↗
576. Xin Yee Tai, Hao Zhang, Zhiqiang Niu, Steven D R Christie, and Jin Xuan. The future of sustainable chemistry and process: Convergence of artificial intelligence, data and hardware. Energy and AI, 2(100036):100036, November 2020. ISSN 2666-5468. doi:10.1016/j.egyai.2020.100036. URL http://dx.doi.org/… — [doi:10.1016/j.egyai.2020.100036](https://doi.org/10.1016/j.egyai.2020.100036) find
577. Chenyu Zheng, Ishita Jalan, Patrick Cost, Kyle Oliver, Anju Gupta, Scott Misture, Jeremy A. Cody, and Christopher J. Collison. Impact of Alkyl Chain Length on Small Molecule Crystallization and Nanomorphology in Squaraine-Based Solution Processed Solar Cells. The Journal of Physical Chemistry C, 121… — [doi:10.1021/acs.jpcc.7b01339](https://doi.org/10.1021/acs.jpcc.7b01339) find

Updated 30 October 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/