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通过智能机器学习分类来推进乱交聚合抑制剂分析

Luxuan Wang¹, Beihong Ji¹, Jingchen Zhai¹

¹Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 3501 Terrace St., Pittsburgh, PA 15261, United States.

Briefings in bioinformatics

|May 7, 2025

概括

机器学习模型准确地识别聚合化合物,减少药物发现中的假阳性. 一种新的解释方法,全球灵敏度分析 (GSA),有效地确定了关键的分子描述符,以改进选图书馆设计.

科学领域:

药用化学医学化学
计算化学计算化学
药物发现药物发现药物发现

背景情况:

小分子在药物发现中至关重要,但可以形成合体聚合物,导致在击中选过程中产生错误阳性.
这些错误的阳性结果增加了研究成本和时间投资,需要早期识别方法.

研究的目的:

开发精确的机器学习分类模型,用于在药物发现过程的早期识别乱交聚合抑制剂.
提出和验证一种新的模型解释方法,全球灵敏度分析 (GSA),用于识别关键预测描述符.

主要方法:

使用10,000个聚合器和10,000个非聚合器的数据集训练了分类模型.
结合了四个分子表示法与各种机器学习算法,包括立方支向量机和基于路径的FP2指纹.
采用了夏普利添加剂的解释,并为模型解释提出了全球灵敏度分析 (GSA).

主要成果:

最好的模型结合了基于路径的FP2指纹和立方支向量机,在验证和测试数据集上实现了高精度和接收器操作特征曲线 (>0.93) 下的面积.
全球敏感性分析 (GSA) 已被证明是识别关键描述符的时间效率高,准确的方法,特别是在大型数据集和有限描述符的情况下.
性能最好的模型保持了高灵敏度和特异性水平 (>0.93).

结论:

机器学习模型,特别是那些使用FP2指纹和立方SVM的机器学习模型,可以有效地识别潜在的聚合化合物,最大限度地减少药物发现中的错误阳性.
GSA为模型解释提供了一种有价值的,高效的方法,补充了现有的方法,如夏普利增量解释.
这些发现为设计查库提供了指导,以减少假阳性和优化药物发现管道.

关键词:

合物聚合器的合物聚合器复合图书馆设计复合图书馆设计药物查对药物进行查.全球敏感性分析机器学习是机器学习.

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