评估分子对接中的小分子形状采样方法
Qiancheng Xia1,2, Qiuyu Fu2, Cheng Shen2
1Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, China.
Journal of computational chemistry
|October 30, 2024
概括
这项研究比较了分子对接的小分子构造采样方法. 不同的方法表现出不同的性能,这表明补充方法可以提高对接精度.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 符合性采样对于准确的分子对接至关重要.
- 存在各种算法,但它们对对接性能的影响尚未完全理解.
研究的目的:
- 在分子对接中评估六种传统和一种基于深度学习的小分子形态采样方法的性能.
- 通过使用各种基准数据集来评估绑定姿势的可复制性和选能力.
主要方法:
- 使用UCSF DOCK 3.7软件进行分子对接.
- 采用了六种传统的方法 (Omega,BCL::Conf,CCD Conformer Generator,ConfGenX,Conformator,RDKit ETKDGv3) 以及扭转扩散 (深度学习) 的方法.
- 与金Diverse,PoseBusters和DUDE-Z数据集进行了对接.
主要成果:
- 不同的形状采样方法显示出不同的对接性能.
- 性能差异归因于特定方法的采样偏好,如二面角范围.
- 没有一种方法在所有指标上普遍优于其他方法.
结论:
- 选择构造性采样方法显著影响分子对接结果.
- 结合互补的采样策略,可以提高对接精度和选功率.
- 对于药物发现应用,对集体采样进行进一步的研究是有必要的.
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