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

Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang +3 more
2/12/2026

No official code found. Best implementation: zjunlp/Mol-Instructions (294 stars, MIT, last commit 2024-10-28).

Abstract

Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.

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MIT

[ICLR 2024] Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

29415PythonApr 12, 20231 years agoMIT
ai-for-sciencebiomedicaldatasetdatasetsgeneration+12 more

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Cite this paper

@article{fang2026molinstructions,
  title  = {Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models},
  author = {Yin Fang and Xiaozhuan Liang and Ningyu Zhang and Kangwei Liu and Rui Huang and Zhuo Chen and Xiaohui Fan and Huajun Chen},
  year   = {2026},
  eprint = {2306.08018},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2306.08018},
  journal = {ICLR 2023 2023}
}

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