PharmacoForge:通过扩散模型生成药
Emma L Flynn1,2, Riya Shah1, Ian Dunn1,2
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States.
Frontiers in bioinformatics
|September 24, 2025
概括
新的扩散模型PharmacoForge产生了基于结构的3D药,用于基于结构的药物设计. 这种方法产生了有效的,商业上可用的分子,提高了虚拟查效率和药物发现.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 基于结构的药物设计 (SBDD) 使用机器学习 (ML) 进行虚拟查和新设计.
- 目前的ML方法在选速度和生成分子的合成可访问性方面存在局限性.
研究的目的:
- 介绍PharmacoForge,这是一个扩散模型,用于生成蛋白质口袋上调节的3D药.
- 通过生成有效的,商业上可用的连接体查询来改进虚拟选.
- 解决现有的基于ML的药物设计策略的局限性.
主要方法:
- 开发了PharmacoForge,这是一个条件扩散模型用于3D药生成.
- 使用LIT-PCBA基准,对PharmacoForge进行了评估,并与自动制药生成进行了对比.
- 通过对DUD-E数据集的回顾性选来评估生成的药.
- 通过对接和应变能量分析,比较了PharmacoForge衍生的连接物与使用de novo生成的连接物.
主要成果:
- 在LIT-PCBA基准测试中,PharmacoForge的表现优于现有的制药生成方法.
- 通过PharmacoForge药识别的配体在对接研究中显示出与新设计的配体相似的性能.
- 通过PharmacoForge产生的分子表现出比de novo设计的分子更低的应变能量.
结论:
- PharmacoForge提供了一种有效的方法来生成3D药,增强基于结构的药物设计.
- 该模型生成有效和合成可访问的分子查询,克服当前生成模型的局限性.
- PharmacoForge代表了ML驱动药物发现的重大进步,提高了效率和可靠性.
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