药物辅助:用于分子优化的大型语言模型

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Briefings in bioinformatics

|January 3, 2025

概括

大型语言模型 (LLM) 现在用于药物发现分子优化. DrugAssist是一种交互式的LLM方法,通过人机对话来增强这个过程,实现领先的结果.

科学领域:

人工智能在药物发现中的作用
计算化学的计算化学
机器学习用于分子优化

背景情况:

大型语言模型 (LLM) 在各种任务中表现出强的性能,促使它们在药物发现中的应用.
分子优化是药物开发中的关键步骤,目前的LLM应用尚未充分探索.
现有的方法往往忽略了专家反和代改进,这是成功药物发现的关键组成部分.

研究的目的:

介绍DrugAssist,一个基于LLM的分子优化的交互式模型.
解决药物发现中非交互式LLM方法的局限性.
为了利用LLM的互动性和通用性,提高分子优化.

主要方法:

开发了DrugAssist,这是一个利用人机对话的交互分子优化模型.
采用LLM的交互性和通用性,用于分子性质的代改进.
在优化循环中利用专家反.

主要成果:

在单个和多个属性优化方面,DrugAssist取得了最先进的结果.
在可转移性和代优化能力方面展示了显著的潜力.
公开发布了"MolOpt-Instructions"数据集,用于分子优化中的LLM微调.

结论:

像DrugAssist这样的交互式LLM方法可以显著提升药物发现中的分子优化.
专家知识与法学士的整合为未来的研究提供了一个有希望的方向.
公共可用的代码和数据将促进LLM驱动药物发现的进一步进展.

关键词:

发现药物的发现.大型语言模型分子优化分子优化

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