可解释的机器学习通过符合性和反事实性预测合理化碳酸酶抑制
Masoumeh Shams Ghamsary1, Milad Rayka1, S Shahab Naghavi2
1Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran, 1983969411, Iran.
Scientific reports
|February 11, 2026
概括
这项研究引入了一个可解释的机器学习框架,用于预测选择性抑制人类碳酸酶 (hCA) 异型 IX 和 XII 的选择性抑制,这是关键的抗癌标. 支持矢量机器展示了卓越的性能,指导了新型hCA抑制剂的设计.
科学领域:
- 生物化学 生物化学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 人类碳酸无水酶 (hCA) 异型 IX 和 XII 是关键的抗癌标.
- 选择性抑制由于与hCA II的结构相似性而具有挑战性,可能导致副作用.
研究的目的:
- 开发一个可解释的机器学习框架来预测和指导hCA异型II,IX和XII的选择性抑制.
- 提高分子解释性和不确定性量化,用于合理的药物设计.
主要方法:
- 严格的数据策划和经典和深度学习模型的系统基准测试.
- 整合合规预测用于不确定性量化.
- 对分子解释性的反事实解释的应用.
主要成果:
- 具有扩展连接指纹的支持向量机器的性能优于复杂模型,突出显示数据质量.
- 符合性预测使严格的活动估计成为可能.
- 反事实分析确定了关键的结构特征,特别是尾部区域,控制着异形选择性.
结论:
- 开发的框架为设计选择性hCA抑制剂提供了可解释的指导.
- 尾部区域是区分hCA异型之间的抑制剂活性的一个关键决定因素.
- 该CAInsight软件促进了选择性hCA抑制剂的虚拟查和生成设计.
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