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Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

Paper · 2023 · Yin Fang, Xiaozhuan Liang, Ningyu Zhang et al.

Summary

General-purpose LLMs perform poorly on biomolecular problems. A key reason is the lack of an instruction-format dataset for the field. Such data is costly to annotate, draws on several disciplines, and uses no single standard representation. Mol-Instructions is a public, instruction-style dataset that addresses this gap. It covers three areas: small molecules, proteins, and biomedical/chemical text. Molecules and proteins are encoded as sequences.

The authors built it in four steps:

  • They wrote seed task descriptions and diversified them with gpt-3.5-turbo, then checked each one manually.
  • They derived labelled examples from established biochemistry databases.
  • They converted structured biological records into text using templates.
  • They applied quality control.

Instruction tuning a base LLM on each part improved results over untuned baselines such as Alpaca-LoRA and Galactica. Gains appeared in property estimation (e.g., HOMO/LUMO), protein function annotation, chemical entity/relation extraction and Q&A. The authors call these models an early demonstration that is not yet production-ready. They also discuss the risk of misuse for harmful bioengineering. The resource matters because it gives LLM research in chemistry, structural biology and drug discovery a shared, open training base.

Key points

  • Mol-Instructions holds 2,043,587 instructions across 17 subtasks and covers more than 11 biomolecular properties. It is released under CC BY-NC-SA 4.0 at github.com/zjunlp/Mol-Instructions.
  • The dataset has three components:
    • Molecule-oriented: 6 tasks, about 1.48M instructions. The extracted text shows "148,4K", but the overall total implies roughly 1.48M.
    • Protein-oriented: about 505K instructions in 5 task categories.
    • Biomolecular text: about 53K instructions in 6 information-extraction and Q&A tasks.
  • Compared with Galactica, PCdes, ChEBI-20, PubChemSTM and MoMu, it is open and designed for instruction tuning rather than pre-training.
  • Baselines included Alpaca-LoRA and Galactica. Alpaca-LoRA gave generic or inaccurate outputs, and Galactica consistently predicted negative property values.
  • The authors propose risk mitigations: regulated access, usage monitoring, community oversight and transparent reporting.

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 worksComputer Vision and Pattern Recognition · 3,486 worksComputational Theory and Mathematics · 321 worksBiochemistry, Genetics and Molecular Biology · 263 worksMolecular Biology · 260 works

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

Cite

Cite as Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan and Huajun Chen. 2023. Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models. arXiv:2306.08018.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2306.08018 · arXiv 2306.08018

@misc{fang2023mol,
  title = {{Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models}},
  author = {Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan and Huajun Chen},
  year = {2023},
  doi = {10.48550/arxiv.2306.08018},
  eprint = {2306.08018},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.08018}
}

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