使用机器学习和元动力学信息的3D分子描述器提高异构功能降解剂的透性预测
Bryant Kim1, Robert P Sheridan1, Ruibo Zhang1
1Modeling and Informatics, Merck & Co., Inc., Rahway, New Jersey 07065, United States.
Journal of chemical information and modeling
|November 13, 2025
概括
三维描述器显著改善了在超出五项规则的化学空间中对异构功能降解剂的被动透性预测. 分子紧度,空间极性和键是设计透化合物的关键预测因素.
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:
- 异生物功能降解剂 (一种向蛋白质降解剂类别) 通常存在于超出五项规则 (bRo5) 的化学空间.
- 使用二维描述符的传统被动透性模型对这些分子的适用性有限.
- 准确预测被动膜透性对于药物设计和开发至关重要.
研究的目的:
- 探索三维 (3D) 分子描述器在bRo5空间中对异构功能降解剂的被动膜透性的预测价值.
- 为了比较机器学习模型使用2D,3D和2D+3D组合描述器集的性能.
- 在这个具有挑战性的化学空间中确定控制被动透性的关键3D特征.
主要方法:
- 在明确的甲中使用温度良好的元动力学生成了符合性合集.
- 通过使用 ANI-2x 神经网络潜力来精炼和波兹曼加权的合奏.
- 三个机器学习模型 (随机森林,PLS,LSVM) 被训练并使用2D,3D和组合描述符集进行评估.
主要成果:
- 包括3D描述器在所有模型中始终改善了预测性能,特别是在bRo5空间.
- 部分最小平方 (PLS) 模型表现最好,交叉验证的R平方从0.29提高到0.48以3D特征.
- 特性重要性分析确定旋转半径 (Rgyr) 为主要的3D描述符,其次是3D极地表面积 (3D-PSA) 和分子内键 (IMHBs).
结论:
- 在物理上有意义的,由集合衍生的3D描述符显著提高了bRo5化学空间中异构功能降解剂的被动透性预测.
- 像Rgyr,3D-PSA和IMHBs这样的3D描述符反映了分子紧性,空间极性和内部键,这些都是透性的关键决定因素.
- 开发的基于珀的分子动力学工作流程广泛适用于评估异构功能降解剂的透性特性.
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