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PyaiVS统一了人工智能工作流程,以加快连接体的发现,并产生ABCG2抑制剂

Mukuo Wang¹, Bojian Qu¹, Lihong Yang¹

¹State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin, 300353, China.

European journal of medicinal chemistry

|September 23, 2025

概括

PyaiVS是一个新的Python包,集成了AI算法,分子表示和数据分割,用于虚拟选. 它有助于优化药物发现模型,从而识别出新的ABCG2抑制剂.

科学领域:

计算化学和化学信息学
人工智能在药物发现中的作用
药理学和药物化学药理学和药物化学

背景情况:

为虚拟选优化的人工智能模型需要仔细选择算法,分子表示和数据分割技术.
现有的工具缺乏整合,阻碍了这些关键组件的协调优化.
开发高效的AI驱动的虚拟查模型对于加速药物发现至关重要.

研究的目的:

介绍PyaiVS,这是一个Python包,旨在整合各种机器学习算法,分子表示和数据分割策略进行虚拟选.
为了证明算法架构,分子表示和数据分割策略的协调优化对人工智能驱动的虚拟选性能的影响.
通过将其与药模拟和对接一起应用来展示PyaiVS的实用性,以识别潜在的ABCG2抑制剂.

主要方法:

PyaiVS集成了9个机器学习算法,5个分子表示 (包括ECFP4/MACCS指纹和基于图表的表示) 和3个数据分割策略 (包括基于集群的分割).
该研究评估了不同组合的算法,表示和分割策略的性能,强调了深度学习模型 (GCN,GAT,注意FP) 和基于集群的分割的有效性,以获得最佳的AUC-ROC.
使用PyaiVS,药模拟和对接,对4,188,623种化合物进行了ABCG2抑制剂的大规模虚拟选.

主要成果:

对算法架构,分子表示和数据分割策略的协调优化对于高效的AI驱动虚拟选至关重要.
基于集群的数据分割实现了AUC-ROC最佳性能68.5%.
实验验证确定了四种新型化合物 (C1/C6/C7/C9),它们与ABCG2结合,分离常数 (kd) 低于100μM,并增强托波特干细胞毒性.

结论:

PyaiVS提供了一个统一且易于访问的平台,用于简化用于虚拟选的优化AI模型的开发.
这些发现强调了选择适合数据集大小和模型架构的适当分子表示和数据分割策略的重要性.
已识别的ABCG2抑制剂证明了PyaiVS在发现潜在治疗药物的实际实用性.

关键词:

这是ABCG2抑制剂.深度学习是一种深度学习.分子表示的分子表示.在Python中使用Python包.虚拟选是一个虚拟的选.

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