有效的诱选择以改进使用机器学习模型的虚拟选
Felipe Victoria-Muñoz1, Janosch Menke1,2, Norberto Sanchez-Cruz3
1Institute of Pharmaceutical and Medicinal Chemistry, Universität Münster, Münster, Germany.
Journal of cheminformatics
|October 31, 2025
概括
有效的诱选择策略对于药物发现中的机器学习模型至关重要. 随机和暗化学物质选择为实际的非结合剂提供了可行的替代品,提高了选能力.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 在药理学中的机器学习.
背景情况:
- 药物发现的机器学习模型严重依赖于蛋白质-连接体相互作用指纹.
- 这些模型的性能严重依赖于所使用的诱选择策略.
- 每个原子的蛋白质分数贡献衍生相互作用指纹 (PADIF) 是模型开发的关键特征.
研究的目的:
- 分析各种诱选择策略,以增强基于PADIF的机器学习模型.
- 评估不同诱源的有效性,包括随机数据库,高通量选非绑定器和对接生成的构造.
- 通过实验确定不活性化合物来验证模型性能.
主要方法:
- 探索了三个诱选择工作流程:随机选择 (ZINC15),反复的非结合物 (暗化学物质) 和数据增强 (对接构造).
- 使用PADIF与ChEMBL的活性分子以及选择的诱方法训练和测试机器学习模型.
- 从LIT-PCBA数据集中对实验确定的无活性化合物进行验证的模型性能.
主要成果:
- 用随机选择ZINC15和暗化学物质化合物训练的模型表现出与使用实际非结合剂的模型相比的性能.
- 所有开发的模型都表现出对特定目标的新化学空间的改进探索.
- 与经典评分功能相比,这些模型增强了顶级活性化合物的选择,增加了分子对接选功率.
结论:
- 从ZINC15随机选择和使用暗化学物质是有效的诱策略,当特定的不活性数据稀缺时.
- 适当的诱选择保持了模型的准确性,并扩大了对新目标的适用性.
- 这些策略显著提高了药物发现分子对接的选能力.
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