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

2023/06/13 by Yin Fang, Fang, Yin, Xiaozhuan Liang +13 · 14 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Machine Learning in Materials Science #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM) #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2306.08018

openalex publication_date 2023/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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• KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
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• BioVERSE: Representation Alignment of Biomedical Modalities to LLMs for Multi-Modal Reasoning
• Enhancing Molecular Property Prediction with Knowledge from Large Language Models
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• A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
• Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation
• AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models
• FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models
• ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge

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• Advancing biomolecular understanding and design following human instructions
• MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension
• Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model
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