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使用MolAnchor分析合理化对异形选择性酸3-激酶抑制剂的预测

Alec Lamens^1,2, Jürgen Bajorath^1,2

¹Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.

Journal of chemical information and modeling

|January 17, 2025

概括

一种新的可解释的人工智能方法MolAnchor识别了用于预测酸酸3-激酶 (PI3K) 抑制剂选择性的关键化学碎片. 这种方法提供了化学直观的解释,通过揭示分子结构和标选择性之间的因果关系来帮助药物发现.

科学领域:

药用化学医学化学
计算化学的计算化学
人工智能在药物发现中的作用

背景情况:

解释机器学习模型预测对于它们在药物发现中的采用至关重要.
酸3-酶 (PI3K) 抑制剂是重要的治疗药物,但实现异型选择性是具有挑战性的.

研究的目的:

开发和验证一种新的方法,MolAnchor,用于生成PI3K抑制剂异型选择性的机器学习预测的化学直观解释.
为了确定负责预测抑制剂选择性的特定结构碎片.

主要方法:

创建一个测试系统来预测PI3K抑制剂异型选择性.
使用MolAnchor方法论对正确预测的系统分析,基于可解释的人工智能""概念.
将MolAnchor解释与其他方法的特征重要性值进行比较.

主要成果:

在大多数情况下,MolAnchor成功地识别了明确的结构碎片,通常是单个子结构,负责预测同型选择性.
对于具有不同异型选择性的抑制剂,发现了明显的反复的亚结构.
与特征重要性值相比,MolAnchor解释显示出更高的解释性.
两个反复出现的子结构与PI3K异型选择性直接相关,这表明存在因果关系.

结论:

该MolAnchor方法为药物发现中的机器学习预测提供了化学直观和可解释的解释.
识别与选择性相关的特定子结构可以指导设计更有选择性的PI3K抑制剂.
这种方法通过阐明化合物活性和选择性的基础,增强了预测建模在药物发现项目中的整合.

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