μOR-连接体:针对μ-阿片类受体连接体功能分类的基于目标感知视图的混合特征选择
1Novexus Ltd, 07058, Antalya, Turkey. s.yavuz.ugurlu@gmail.com.
Journal of computer-aided molecular design
|October 24, 2025
概括
本研究引入了μOR-Ligand框架,通过整合连接体和标相互作用特征,改进了人类μ-阿片类受体 (μOR) 的agonist与antagonist分类. 这种新的方法提高了药物发现和安全性评估的准确性.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 在生物信息学中的机器学习
背景情况:
- 将μ-阿片类受体 (μOR) 配体准确分类为激动剂或对抗剂对于药物发现和安全至关重要.
- 现有的机器学习模型,如ExtraTrees和MPNNs显示出希望,但在理解特征影响和评估稳定性方面存在局限性.
- 目标条件互动特征和重新采样方法 (例如,SMOTE) 对模型性能的影响需要进一步调查.
研究的目的:
- 引入μOR-Ligand框架,一种基于目标的,基于视图的混合特征选择方法,用于改进μOR函数类预测.
- 为了提高识别是否一个活跃的配体作为一个激进分子或对抗剂.
- 为 μOR 建模建立一个强大的评估协议,控制重新采样效应.
主要方法:
- 开发了μOR-Ligand框架,利用三种观点:联体指纹,联体描述符和分子相互作用特征.
- 采用混合特征选择策略,将多模型融合和组合特征选择 (堆叠) 结合在一起,用于堆叠的组合.
- 实施了重新采样控制的评估协议,使用相同的,有或没有SMOTE的固定分割进行稳定性评估.
主要成果:
- OR-利甘德框架实现了0.930 ± 0.026的优异ROC AUC,超过了MPNNs (p值=0.046) 等最近的模型.
- 在内部交叉验证中实现了0.977的高ROC AUC,证明了强大的预测性能.
- 证明了针对目标的交互功能提供了互补信号,改善了分类性能和稳定性.
结论:
- 通过μOR-Ligand框架将基于联体和目标条件的视图混合,可以显著提高μOR联体的功能分类准确性.
- 该研究为μOR建模建立了一个标准化,重新采样控制的评估协议.
- 确定了顶级预测特征和μOR口袋化学之间的相关性,为未来的药物设计提供了洞察力.
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