评估可扩展的监督学习,用于按需合成的化学库

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Updated: Jul 18, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Moayad Alnammi^1,2,3, Shengchao Liu^1,2, Spencer S Ericksen⁴

¹Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

Journal of chemical information and modeling

|August 25, 2023

概括

机器学习驱动的虚拟选成功地在大型化学库中识别了细菌PriA-SSB目标的活性化合物. 这种方法显著优于传统方法,加速了药物发现.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
机器学习是机器学习.

背景情况:

传统的药物发现是缓慢而昂贵的,高通量选只检查一个小的化学空间.
机器学习 (ML) 为大型化学库提供了强大的虚拟选 (VS) 功能,但缺乏广泛的实验验证.

研究的目的:

预期评估基于连接体的虚拟查,使用ML识别针对细菌蛋白质-蛋白质相互作用的活性化合物PriA-SSB.
为了比较不同ML模型的性能,并验证大型商业和定制化学图书馆的发现.

主要方法:

交叉验证用于比较监督学习模型,选择一个随机森林 (RF) 分类器作为最佳.
射频模型选了来自Aldrich Market Select的800多万种化合物和来自Enamine REAL数据库的10亿种化合物.
实验验证是在两个图书馆中选择的化合物上进行的.

主要成果:

射频模型显著优于结构相似性基线,在701个选定的化合物中,48%的化合物显示对PriA-SSB的活性.
从Enamine REAL测试68个不同的顶级预测结果,得到31个结果 (46%),其中一个是1.3μM IC50.
ML方法在识别新型活性化合物方面展示了可扩展性和有效性.

结论:

基于联体的虚拟选由ML,特别是RF驱动,是从庞大的化学库中发现活性小分子的高效策略.
这种经过验证的ML方法加速了候选药物的识别,克服了传统选方法的局限性.
该研究强调了ML驱动的VS在现实世界药物发现计划中的实用实用性.

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