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.

中文翻译：

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评估分子对接中的小分子构象采样方法


小分子构象采样在分子对接中起着关键作用。最近的进展导致了各种构象采样方法的出现，每种方法都采用不同的算法。本研究使用 UCSF DOCK 3.7 调查了不同小分子构象采样方法对分子对接的影响。具体来说，评估了六种传统采样方法 （Omega、BCL：：Conf、CCDC Conformer Generator、ConfGenX、Conformator、RDKit ETKDGv3） 和用于生成构象集合的基于深度学习的模型 （Torsional Diffusion）。这些融合随后与 Platinum Diverse 数据集、PoseBusters 数据集和 DUDE-Z 数据集对接，以评估结合姿势的可重复性和筛选能力。值得注意的是，不同的采样方法由于其独特的偏好而表现出不同的性能，例如可旋转键的二面角采样范围。结合互补方法可能会进一步提高对接性能。