---
title: "Mol-Instructions — paper by Fang et al. (2023)"
description: "General-purpose LLMs perform poorly on biomolecular problems. 76 references, cited by 58 papers here."
authors: ["Yin Fang", "Xiaozhuan Liang", "Ningyu Zhang", "Kangwei Liu", "Rui Huang", "Zhuo Chen", "Xiaohui Fan", "Huajun Chen"]
year: 2023
venue: "arXiv:2306.08018"
source: "https://arxiv.org/abs/2306.08018"
doi: "10.48550/arxiv.2306.08018"
arxiv: "2306.08018"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/fang-2023-mol-instructions-large-scale-biomolecular/"
---
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# Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

**Authors:** Yin Fang, [Xiaozhuan Liang](https://md.research.elamur.ai/free/en/authors/xiaozhuan-liang/), [Ningyu Zhang](https://md.research.elamur.ai/free/en/authors/ningyu-zhang/), Kangwei Liu, Rui Huang, [Zhuo Chen](https://md.research.elamur.ai/free/en/authors/zhuo-chen/), Xiaohui Fan and [Huajun Chen](https://md.research.elamur.ai/free/en/authors/huajun-chen/) · **Year:** 2023 · **Venue:** arXiv:2306.08018 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2306.08018) · [PDF](https://arxiv.org/pdf/2306.08018) · [DOI 10.48550/arxiv.2306.08018](https://doi.org/10.48550/arxiv.2306.08018) · [arXiv 2306.08018](https://arxiv.org/abs/2306.08018) · [OpenAlex](https://openalex.org/W4380989429)

**Citations:** 33 citations (OpenAlex, 2026-10-14) · [58](https://md.research.elamur.ai/free/en/papers/fang-2023-mol-instructions-large-scale-biomolecular/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## 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.*

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## 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.

```bibtex
@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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*Updated 2 November 2026*