在深度学习中利用角形几何学来预测蛋白质-配体结合亲和力
Julia Rahman1, M A Hakim Newton2, Jiffriya Mohamed Abdul Cader3
1Griffith University, 170 Kessels Rd, Nathan, 4111, QLD, Australia; Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Computer methods and programs in biomedicine
|February 15, 2026
概括
这项研究引入了角度几何特征,用于预测蛋白质-连接体结合亲和力,实现了最先进的结果. 角度感知预测器 (AAP) 为药物发现提供了一种轻量级且有效的方法.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 在基于结构的药物设计中,蛋白质 - 配体结合性亲和力预测至关重要.
- 当前的深度学习模型通常依赖于资源密集的3D网格或分子图.
- 这些方法可能无法完全捕捉绑定所必需的特定定向相互作用.
研究的目的:
- 引入新的角度几何特征作为绑定相互作用的描述符.
- 开发一种有效和轻量级的模型,用于结合亲和力预测.
主要方法:
- 提取蛋白质和配体原子之间的七种二面角类型,以编码导向和几何.
- 开发一个完全连接的整体网络,即角度感知预测器 (AAP),以整合这些角度特征.
主要成果:
- 在CASF-2016基准指标上,AAP取得了最先进的表现,R=0.872,RMSE=1.072,MAE=0.817,SD=1.077,CI=0.845.
- 在四个额外的基准数据集上观察到持续的性能改进,范围从0.3%到36%.
- 该模型展示了有效性,稳定性和计算效率.
结论:
- 角形几何特征是有效的,轻量级的,强大的描述符,用于绑定亲和力预测.
- 角形几何学代表了未来基于结构的药物发现的有希望的方向.
- 该AAP程序和数据是公开可用的,用于进一步的研究和应用.
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