使用机器学习方法预测西格玛受体连接体的活性和选择性概况
Lisa Lombardo1, Verena Battisti2, Thierry Langer2
1Department of Chemical, Biological, Pharmaceutical and Environmental Sciences (CHIBIOFARAM), University of Messina, Viale Ferdinando D'Alcontres 13, I-98166 Messina, Italy.
Journal of chemical information and modeling
|September 1, 2025
概括
机器学习模型预测了西格玛受体连接体活性和选择性. 使用额外树和Mordred描述符的多分类方法被证明是最有效的选择性S1R和S2R联体的识别方法.
科学领域:
- 药理学和化学信息学
- 计算机药物发现
背景情况:
- 西格玛受体 (SRs),包括S1R (中枢神经系统,神经保护) 和S2R (癌症,神经元),是关键的治疗点.
- 尽管存在结构上的差异,但SR亚型具有共同的结合部位特征,因此需要针对性治疗的选择性连接物鉴定.
研究的目的:
- 开发和评估用于预测西格玛受体连接体活性和选择性的机器学习模型.
- 确定最有效的计算工作流程来区分S1R和S2R联体.
主要方法:
- 对西格玛受体配体进行精选的高质量数据集.
- 使用了三种机器学习方法:分类,回归和多重分类.
- 使用了分子描述器 (Mordred,RDKit) 和指纹 (ECFP4,ECFP6,MACCS) 与其他树木,随机森林,SVM,k-NN和XGBoost等算法.
- 通过嵌套和5倍交叉验证验证模型,然后进行外部验证.
主要成果:
- 一步多重分类方法,将额外树与Mordred描述符集成,显示出优异的预测性能.
- 这种工作流在外部验证中取得了最好的结果,显示出强大的预测能力.
结论:
- 机器学习,特别是多重分类,为预测SR连接体配置提供了强大的工具.
- 开发的Extra Trees-Mordred工作流提供了一种可靠的方法来识别选择性的S1R和S2R连接物,帮助治疗设计.
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