Elamur Research⌕
☰
home / papers / 2023 / ChemCrow: Augmenting large-language models with chemistry tools

ChemCrow: Augmenting large-language models with chemistry tools

Paper · 2023 · Andres M Bran, Sam Cox, Oliver Schilter et al.

Summary

General-purpose LLMs such as GPT-4 often fail at chemistry. They cannot reliably convert IUPAC names into structures, and they have no access to specialised software or databases. Meanwhile, many strong computational chemistry tools exist, but non-programmers find them hard to combine. ChemCrow addresses both problems with an agent built on GPT-4 that uses 18 expert-designed tools. These cover molecule handling, reactions, safety, search and general utilities. The agent follows the ReAct/MRKL loop: it reasons about the next step, calls a tool, reads the output and repeats until it can answer.

The authors tested ChemCrow on 14 use cases in synthesis, drug discovery and materials design. Through IBM's cloud-connected RoboRXN robot, it planned and ran real syntheses, fixing invalid robot procedures without human help. Working with a chemist, it also helped find a new chromophore. Both LLM and expert judges assessed the outputs. GPT-4 as a judge could not tell clearly wrong plain GPT-4 answers apart from ChemCrow's. The work shows that tool-augmented LLMs can link computational and experimental chemistry. It also suggests that LLM-based evaluation is unreliable in specialised domains.

Key points

  • ChemCrow runs on GPT-4 with 18 tools, such as Name2SMILES, LitSearch/WebSearch, NameRXN, and ReactionPredict, ReactionPlanner and ReactionExecute (based on the RXN4Chemistry API). An open-source release on GitHub (ur-whitelab/chemcrow-public) includes 12 of these tools.
  • On RoboRXN, the agent independently synthesised the insect repellent DEET and three thiourea organocatalysts: Schreiner's, Ricci's and Takemoto's. All four gave the expected products.
  • In the chromophore case, ChemCrow trained a Random Forest on Morgan fingerprints (RMSE 37 nm) and picked a candidate predicted to absorb near 369 nm. After synthesis, the measured absorption maximum was 336 nm.
  • Safety tools check molecules against OPCW and Australia Group chemical-weapon lists, flag explosives via GHS/PubChem, and generate safety summaries. The agent halts when a request involves a listed weapon or precursor.
  • The evaluation used 14 use cases with both expert and LLM judges. GPT-4 as an evaluator failed to separate wrong GPT-4 completions from ChemCrow's answers.

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

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksComputational Theory and Mathematics · 321 worksMaterials Science · 31 worksMaterials Chemistry · 31 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White and Philippe Schwaller. 2023. ChemCrow: Augmenting large-language models with chemistry tools. arXiv:2304.05376.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2304.05376 · arXiv 2304.05376

@misc{bran2023chemcrow,
  title = {{ChemCrow: Augmenting large-language models with chemistry tools}},
  author = {Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White and Philippe Schwaller},
  year = {2023},
  doi = {10.48550/arxiv.2304.05376},
  eprint = {2304.05376},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2304.05376}
}

References

108 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 2018,
  2. Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems 2020, 33, 1877–1901.
  3. Bommasani, R.; Hudson, D. A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M. S.; Bohg, J.; Bosselut, A.; Brunskill, E., et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 2021,
  4. Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S., et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 2022,
  5. Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y. T.; Li, Y.; Lundberg, S., et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 2023,
  6. GitHub Copilot: Your AI pair programmer. https://copilot.github.com. ↗
  7. Li, R. et al. StarCoder: may the source be with you! 2023. find
  8. Ziegler, A.; Kalliamvakou, E.; Li, X. A.; Rice, A.; Rifkin, D.; Simister, S.; Sittampalam, G.; Aftandilian, E. Productivity assessment of neural code completion. 2022, 21–29. find
  9. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Advances in neural information processing systems 2017, 30.
  10. Schick, T.; Dwivedi-Yu, J.; Dessì, R.; Raileanu, R.; Lomeli, M.; Zettlemoyer, L.; Cancedda, N.; Scialom, T. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 2023,
  11. Castro Nascimento, C. M.; Pimentel, A. S. Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling 2023, 63, 1649–1655. find
  12. OpenAI, GPT-4 Technical Report. 2023.
  13. Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A., et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 2022, 35, 27730–27744.
  14. White, A. D.; Hocky, G. M.; Gandhi, H. A.; Ansari, M.; Cox, S.; Wellawatte, G. P.; Sasmal, S.; Yang, Z.; Liu, K.; Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2023, find
  15. Lowe, D. M.; Corbett, P. T.; Murray-Rust, P.; Glen, R. C. Chemical Name to Structure: OPSIN, an Open Source Solution. Journal of Chemical Information and Modeling 2011, 51, 739–753, PMID: 21384929. find
  16. Coley, C. W.; Barzilay, R.; Jaakkola, T. S.; Green, W. H.; Jensen, K. F. Prediction of organic reaction outcomes using machine learning. ACS central science 2017, 3, 434–443. find
  17. Coley, C. W.; Jin, W.; Rogers, L.; Jamison, T. F.; Jaakkola, T. S.; Green, W. H.; Barzilay, R.; Jensen, K. F. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 2019, 10, 370–377. find
  18. Schwaller, P.; Laino, T.; Gaudin, T.; Bolgar, P.; Hunter, C. A.; Bekas, C.; Lee, A. A. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS central science 2019, 5, 1572–1583. find
  19. Pesciullesi, G.; Schwaller, P.; Laino, T.; Reymond, J.-L. Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates. Nat. Commun. 2020, 11, 1–8. find
  20. Irwin, R.; Dimitriadis, S.; He, J.; Bjerrum, E. J. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology 2022, 3, 015022. find
  21. Szymkuć, S.; Gajewska, E. P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B. A. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. - Int. Ed. 2016, 55, 5904–5937. find
  22. Segler, M. H.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018, 555, 604–610. find
  23. Coley, C. W.; Thomas, D. A.; Lummiss, J. A.; Jaworski, J. N.; Breen, C. P.; Schultz, V.; Hart, T.; Fishman, J. S.; Rogers, L.; Gao, H., et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 2019, 365. find
  24. Schwaller, P.; Petraglia, R.; Zullo, V.; Nair, V. H.; Haeuselmann, R. A.; Pisoni, R.; Bekas, C.; Iuliano, A.; Laino, T. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chemical science 2020, 11, 3316–3325. find
  25. Genheden, S.; Thakkar, A.; Chadimová, V.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E. AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminf. 2020, 12, 1–9. find
  26. Molga, K.; Szymkuć, S.; Grzybowski, B. A. Chemist Ex Machina: Advanced Synthesis Planning by Computers. Acc. Chem. Res. 2021, 54, 1094–1106. find
  27. Schwaller, P.; Vaucher, A. C.; Laplaza, R.; Bunne, C.; Krause, A.; Corminboeuf, C.; Laino, T. Machine intelligence for chemical reaction space. Wiley Interdisciplinary Reviews: Computational Molecular Science 2022, 12, e1604. find
  28. Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: toxicity prediction using deep learning. Frontiers in Environmental Science 2016, 3, 80. find
  29. Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M., et al. Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling 2019, 59, 3370–3388. find
  30. Chithrananda, S.; Grand, G.; Ramsundar, B. Chemberta: Large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 2020, find
  31. van Tilborg, D.; Alenicheva, A.; Grisoni, F. Exposing the limitations of molecular machine learning with activity cliffs. Journal of Chemical Information and Modeling 2022, 62, 5938–5951. find
  32. Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B. Is GPT-3 all you need for low-data discovery in chemistry? 2023, find
  33. Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2018, 4, 268–276… find
  34. Blaschke, T.; Arús-Pous, J.; Chen, H.; Margreitter, C.; Tyrchan, C.; Engkvist, O.; Papadopoulos, K.; Patronov, A. REINVENT 2.0: an AI tool for de novo drug design. Journal of chemical information and modeling 2020, 60, 5918–5922. find
  35. Tao, Q.; Xu, P.; Li, M.; Lu, W. Machine learning for perovskite materials design and discovery. npj Computational Materials 2021, 7, 1–18, Number: 1 Publisher: Nature Publishing Group. find
  36. Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nature Materials 2016, 15, 1120–1127, Number: 10 Publisher: Nature Publishing Group. find
  37. Shields, B. J.; Stevens, J.; Li, J.; Parasram, M.; Damani, F.; Alvarado, J. I. M.; Janey, J. M.; Adams, R. P.; Doyle, A. G. Bayesian reaction optimization as a tool for chemical synthesis. Nature 2021, 590, 89–96. find
  38. Torres, J. A. G.; Lau, S. H.; Anchuri, P.; Stevens, J. M.; Tabora, J. E.; Li, J.; Borovika, A.; Adams, R. P.; Doyle, A. G. A Multi-Objective Active Learning Platform and Web App for Reaction Optimization. Journal of the American Chemical Society 2022, 144, 19999–20007. find
  39. Ramos, M. C.; Michtavy, S. S.; Porosoff, M. D.; White, A. D. Bayesian Optimization of Catalysts With In-context Learning. arXiv preprint arXiv:2304.05341 2023, find
  40. Marra, G.; Giannini, F.; Diligenti, M.; Gori, M. Integrating learning and reasoning with deep logic models. 2020, 517–532. find
  41. Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Chi, E.; Le, Q.; Zhou, D. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 2022,
  42. Ho, N.; Schmid, L.; Yun, S.-Y. Large Language Models Are Reasoning Teachers. arXiv preprint arXiv:2212.10071 2022,
  43. Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; Cao, Y. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629 2022,
  44. Zelikman, E.; Wu, Y.; Mu, J.; Goodman, N. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems 2022, 35, 15476–15488.
  45. Zhao, Z.-W.; del Cueto, M.; Troisi, A. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors. Digital Discovery 2022, 1, 266–276. find
  46. Vaucher, A. C.; Schwaller, P.; Geluykens, J.; Nair, V. H.; Iuliano, A.; Laino, T. Inferring experimental procedures from text-based representations of chemical reactions. Nature communications 2021, 12, 2573. find
  47. Schwaller, P.; Probst, D.; Vaucher, A. C.; Nair, V. H.; Kreutter, D.; Laino, T.; Reymond, J.-L. Mapping the space of chemical reactions using attention-based neural networks. Nature machine intelligence 2021, 3, 144–152. find
  48. rxn4Chemistry, rxn4Chemistry. 2020; Accessed: April 2023. https://github.com/rxn4chemistry/rxn4chemistry, ↗
  49. Thakkar, A.; Kogej, T.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E. J. Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chemical science 2020, 11, 154–168. find
  50. Thakkar, A.; Selmi, N.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E. J. “Ring breaker”: neural network driven synthesis prediction of the ring system chemical space. Journal of medicinal chemistry 2020, 63, 8791–8808. find
  51. Yang, Z.; Li, L.; Wang, J.; Lin, K.; Azarnasab, E.; Ahmed, F.; Liu, Z.; Liu, C.; Zeng, M.; Wang, L. MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action. arXiv preprint arXiv:2303.11381 2023,
  52. Shen, Y.; Song, K.; Tan, X.; Li, D.; Lu, W.; Zhuang, Y. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace. 2023. find
  53. Karpas, E.; Abend, O.; Belinkov, Y.; Lenz, B.; Lieber, O.; Ratner, N.; Shoham, Y.; Bata, H.; Levine, Y.; Leyton-Brown, K., et al. MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. arXiv preprint arXiv:2205.004… — [arXiv:2205.00445](https://arxiv.org/abs/2205.00445) find
  54. Boiko, D. A.; MacKnight, R.; Gomes, G. Emergent autonomous scientific research capabilities of large language models. arXiv preprint 2023,
  55. IBM RoboRXN \| Science \| IBM Research — research.ibm.com. https://research.ibm.com/science/ibm-roborxn/, [Accessed 12-May-2023]. ↗
  56. Wittkopp, A.; Schreiner, P. R. Metal-Free, Noncovalent Catalysis of Diels–Alder Reactions by Neutral Hydrogen Bond Donors in Organic Solvents and in Water. Chemistry – A European Journal 2003, 9, 407–414. find
  57. Schreiner, P. R.; Wittkopp, A. H-Bonding Additives Act Like Lewis Acid Catalysts. Organic Letters 2002, 4, 217–220, Publisher: American Chemical Society. find
  58. Herrera, R. P.; Sgarzani, V.; Bernardi, L.; Ricci, A. Catalytic Enantioselective Friedel–Crafts Alkylation of Indoles with Nitroalkenes by Using a Simple Thiourea Organocatalyst. Angewandte Chemie International Edition 2005, 44, 6576–6579. find
  59. Okino, T.; Hoashi, Y.; Takemoto, Y. Enantioselective Michael Reaction of Malonates to Nitroolefins Catalyzed by Bifunctional Organocatalysts. Journal of the American Chemical Society 2003, 125, 12672–12673, Publisher: American Chemical Society. find
  60. Lowe, D. M. Extraction of chemical structures and reactions from the literature. Ph.D. thesis, University of Cambridge, 2012. find
  61. Wu, Z.; Ramsundar, B.; Feinberg, E. N.; Gomes, J.; Geniesse, C.; Pappu, A. S.; Leswing, K.; Pande, V. MoleculeNet: a benchmark for molecular machine learning. Chemical science 2018, 9, 513–530. find
  62. Liu, Y.; Iter, D.; Xu, Y.; Wang, S.; Xu, R.; Zhu, C. GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment. arXiv preprint arXiv:2303.16634 2023,
  63. Eloundou, T.; Manning, S.; Mishkin, P.; Rock, D. Gpts are gpts: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130 2023, — [arXiv:2303.10130](https://arxiv.org/abs/2303.10130) find
  64. Grzybowski, B. A.; Badowski, T.; Molga, K.; Szymkuć, S. Network search algorithms and scoring functions for advanced-level computerized synthesis planning. WIREs Computational Molecular Science 2023, 13, e1630. find
  65. Thakkar, A.; Johansson, S.; Jorner, K.; Buttar, D.; Reymond, J.-L.; Engkvist, O. Artificial intelligence and automation in computer aided synthesis planning. Reaction chemistry & engineering 2021, 6, 27–51. find
  66. Urbina, F.; Lentzos, F.; Invernizzi, C.; Ekins, S. Dual use of artificial-intelligence-powered drug discovery. Nature Machine Intelligence 2022, 4, 189–191. find
  67. Urbina, F.; Lentzos, F.; Invernizzi, C.; Ekins, S. A teachable moment for dual-use. Nature machine intelligence 2022, 4, 607–607. find
  68. Campbell, Q. L.; Herington, J.; White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 2023, — [arXiv:2304.10510](https://arxiv.org/abs/2304.10510) find
  69. Gao, L.; Schulman, J.; Hilton, J. Scaling Laws for Reward Model Overoptimization. arXiv preprint arXiv:2210.10760 2022,
  70. Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I., et al. Improving language understanding by generative pre-training. 2018, find
  71. Li, B.; Qi, P.; Liu, B.; Di, S.; Liu, J.; Pei, J.; Yi, J.; Zhou, B. Trustworthy AI: From Principles to Practices. ACM Computing Surveys 2021, 55, 1 – 46. find
  72. Hocky, G. M.; White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital Discovery 2022, 1, 79–83. find
  73. Henderson, P.; Li, X.; Jurafsky, D.; Hashimoto, T.; Lemley, M. A.; Liang, P. Foundation Models and Fair Use. arXiv preprint arXiv:2303.15715 2023, find
  74. Askell, A.; Brundage, M.; Hadfield, G. The Role of Cooperation in Responsible AI Development. 2019. find
  75. Neufville, R. d.; Baum, S. D. Collective action on artificial intelligence: A primer and review. Technology in Society 2021, 66, 101649. find
  76. Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; Rodriguez, A.; Joulin, A.; Grave, E.; Lample, G. LLaMA: Open and Efficient Foundation Language Models. 2023.
  77. Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; Xing, E. P. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. 2023; https://lmsys.org/blog/2023-03-30-vicuna/. ↗
  78. Mukherjee, S.; Mitra, A.; Jawahar, G.; Agarwal, S.; Palangi, H.; Awadallah, A. Orca: Progressive Learning from Complex Explanation Traces of GPT-4. 2023.
  79. Chase, H. LangChain. 2022; https://github.com/hwchase17/langchain. ↗
  80. Press, O.; Zhang, M.; Min, S.; Schmidt, L.; Smith, N. A.; Lewis, M. Measuring and Narrowing the Compositionality Gap in Language Models. arXiv preprint arXiv:2210.03350 2022, find
  81. SerpAPI, SerpAPI - Google Search Results API. 2023; https://serpapi.com/. ↗
  82. Neelakantan, A.; Xu, T.; Puri, R.; Radford, A.; Han, J. M.; Tworek, J.; Yuan, Q.; Tezak, N.; Kim, J. W.; Hallacy, C., et al. Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005 2022,
  83. Johnson, J.; Douze, M.; Jégou, H. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data 2019, 7, 535–547.
  84. ChemSpace, ChemSpace - Provider of Chemical Building Blocks, Fragment Libraries, and Screening Compounds. https://chem-space.com/, 2023. ↗
  85. National Center for Biotechnology Information, PubChem. https://pubchem.ncbi.nlm.nih.gov/, 2023. ↗
  86. Lowe, D. M.; Corbett, P. T.; Murray-Rust, P.; Glen, R. C. Chemical name to structure: OPSIN, an open source solution. 2011. find
  87. Medina, J.; White, A. D. Bloom filters for molecules. arXiv preprint arXiv:2304.05386 2023, — [arXiv:2304.05386](https://arxiv.org/abs/2304.05386) find
  88. Irwin, J. J.; Tang, K. G.; Young, J.; Dandarchuluun, C.; Wong, B. R.; Khurelbaatar, M.; Moroz, Y. S.; Mayfield, J.; Sayle, R. A. ZINC20—a free ultralarge-scale chemical database for ligand discovery. Journal of chemical information and modeling 2020, 60, 6065–6073. find
  89. Chemical Abstracts Service, CAS Registry Number. https://www.cas.org/content/cas-registry, Accessed: April 2023. ↗
  90. TT, T. An elementary mathematical theory of classification and prediction; 1958. find
  91. Rogers, D.; Hahn, M. Extended-connectivity fingerprints. Journal of chemical information and modeling 2010, 50, 742–754. find
  92. White, Andrew D, SynSpace. https://github.com/whitead/synspace, Accessed: April 2023; GitHub repository. ↗
  93. Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 2022, 13, 3697–3705. find
  94. MedChemComm. Accessed: 5 April 2023, find
  95. Yang, Q.; Sresht, V.; Bolgar, P.; Hou, X.; Klug-McLeod, J. L.; Butler, C. R., et al. Molecular transformer unifies reaction prediction and retrosynthesis across pharma chemical space. Chemical communications 2019, 55, 12152–12155. find
  96. Mcule, Purchasable Mcule. https://purchasable.mcule.com/, Accessed: April 2023. ↗
  97. RDKit: Open-source cheminformatics. http://www.rdkit.org, [Online; accessed April 2023]. ↗
  98. Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org, Accessed: May 2023. ↗
  99. Group, A. Export Control List: Chemical Weapons Precursors. https://australiagroup.net/en/, Accessed: May 2023. ↗
  100. NextMove Software, NameRxn. https://www.nextmovesoftware.com/namerxn.html, last accessed 2020/11/07; Accessed: 5 April 2023. ↗
  101. Carey, J. S.; Laffan, D.; Thomson, C.; Williams, M. T. Analysis of the reactions used for the preparation of drug candidate molecules. Organic & biomolecular chemistry 2006, 4, 2337–2347. find
  102. Vaucher, A. C.; Schwaller, P.; Geluykens, J.; Nair, V. H.; Iuliano, A.; Laino, T. Inferring experimental procedures from text-based representations of chemical reactions. Nature Communications 2021, 12, 2573, Number: 1 Publisher: Nature Publishing Group. find
  103. Edwards, C.; Lai, T.; Ros, K.; Honke, G.; Cho, K.; Ji, H. Translation between Molecules and Natural Language. 2022.
  104. Christofidellis, D.; Giannone, G.; Born, J.; Winther, O.; Laino, T.; Manica, M. Unifying Molecular and Textual Representations via Multi-task Language Modelling. arXiv preprint arXiv:2301.12586 2023, — [arXiv:2301.12586](https://arxiv.org/abs/2301.12586) find
  105. Azamfirei, R.; Kudchadkar, S. R.; Fackler, J. Large language models and the perils of their hallucinations. Critical Care 2023, 27, 1–2. find
  106. Khapra, M. M.; Sai, A. B. A tutorial on evaluation metrics used in natural language generation. 2021, 15–19. find
  107. Melis, G.; Dyer, C.; Blunsom, P. On the state of the art of evaluation in neural language models. arXiv preprint arXiv:1707.05589 2017, find
  108. Flam-Shepherd, D.; Zhu, K.; Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nature Communications 2022, 13, 3293. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/bran-2023-chemcrow-augmenting-large-language/