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Large Language Model Agent for Modular Task Execution in Drug Discovery

delete2026-02-01
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OA
AI
J
Janghoon Ock
R
Radheesh Sharma Meda
S
Srivathsan Badrinarayanan
N
Neha S. Aluru
A
Achuth Chandrasekhar
A
Amir Barati Farimani *
DOI：10.1021/acs.jcim.5c02454delete
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摘要

摘要

En 中文
我们提出了一种由大型语言模型（LLMs）驱动的模块化框架，该框架自动化并简化了早期计算药物发现流程中的关键任务。通过结合LLM推理与领域特定工具，该框架执行生物医学数据检索、基于检索增强生成的文献支撑问答、分子生成、多属性预测、属性感知分子优化以及3D蛋白质-配体结构生成。该智能体自主检索相关的生物分子信息，包括FASTA序列、SMILES表示和文献，并以比标准LLMs更高的上下文准确性回答机制性问题。随后，它生成化学多样性种子分子并预测75种属性，包括与ADMET相关的和一般物理化学描述符，这些属性指导迭代分子优化。经过两轮优化，QED>0.6的分子数量从34个增加到55个。满足经验药物相似性过滤器的分子数量也有所增加；例如，在100个分子的池中，Ghose过滤器的符合数量从32个增加到55个。该框架还采用Boltz-2生成3D蛋白质-配体复合物，并为候选化合物提供快速的结合亲和力估计。这些结果表明，该方法有效支持分子筛选、优先级排序和结构评估。其模块化设计使工具和模型的灵活集成成为可能，为AI辅助疗法发现提供了可扩展的基础。
Keyword:
MOLECULAR-DYNAMICS
PERMEABILITY
PREDICTION
PROTEIN

期刊

Journal of Chemical Information and Modeling 封面图
Journal of Chemical Information and Modeling
IF:
5.3
论文数:
9.1K
被引数:
4.0W

机构

U
university of nebraska lincoln
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carnegie mellon university
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2.1K
论文数: 991
被引数: 0
T
Texas A&M University System
学者数:
4.4W
论文数: 4.0W
被引数: 4.0K
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