Assessing small molecule conformational sampling methods in molecular docking

IF 3.4 3区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Journal of Computational Chemistry Pub Date : 2024-10-30 DOI:10.1002/jcc.27516

Qiancheng Xia, Qiuyu Fu, Cheng Shen, Ruth Brenk, Niu Huang

引用次数: 0

Abstract

Small molecule conformational sampling plays a pivotal role in molecular docking. Recent advancements have led to the emergence of various conformational sampling methods, each employing distinct algorithms. This study investigates the impact of different small molecule conformational sampling methods in molecular docking using UCSF DOCK 3.7. Specifically, six traditional sampling methods (Omega, BCL::Conf, CCDC Conformer Generator, ConfGenX, Conformator, RDKit ETKDGv3) and a deep learning-based model (Torsional Diffusion) for generating conformational ensembles are evaluated. These ensembles are subsequently docked against the Platinum Diverse Dataset, the PoseBusters dataset and the DUDE-Z dataset to assess binding pose reproducibility and screening power. Notably, different sampling methods exhibit varying performance due to their unique preferences, such as dihedral angle sampling ranges on rotatable bonds. Combining complementary methods may lead to further improvements in docking performance.

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评估分子对接中的小分子构象取样方法

小分子构象取样在分子对接中起着举足轻重的作用。最近的进步导致了各种构象取样方法的出现，每种方法都采用了不同的算法。本研究使用 UCSF DOCK 3.7 研究了不同小分子构象取样方法对分子对接的影响。具体来说，本研究评估了六种传统采样方法（Omega、BCL::Conf、CCDC Conformer Generator、ConfGenX、Conformator、RDKit ETKDGv3）和一种基于深度学习的模型（扭转扩散），用于生成构象合集。随后将这些构象组合与 Platinum Diverse 数据集、PoseBusters 数据集和 DUDE-Z 数据集进行对接，以评估结合姿态的重现性和筛选能力。值得注意的是，不同的取样方法因其独特的偏好（如可旋转键的二面角取样范围）而表现出不同的性能。结合互补方法可能会进一步提高对接性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Chemistry 化学-化学综合

CiteScore

6.60

自引率

3.30%

发文量

247

审稿时长

1.7 months

期刊介绍： This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.