Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind.

确定配体的结合姿势是许多计算机药物发现任务中关键的第一步。分子对接是预测蛋白质和配体系统非共价结合的主要工具。尽管普遍认为不同配体在与同一受体结合时共享结合相互作用，但分子对接管道通常仅利用一种配体与蛋白质结合的信息。在这里，我们描述了 Open-ComBind，这是 ComBind 分子对接管道的易于使用的开源版本，它利用来自多个没有已知结合结构的配体的信息来增强姿势选择。我们首先创建配体姿势对之间的特征相似性分布，将接近自然的姿势与所有采样的对接姿势进行比较。这些分布捕获了在不同姿势配置中观察到相似特征的可能性，例如氢键或疏水接触。然后将这些相似性分布与每个配体对接分数相结合，以将高亲和力和同系系列辅助配体的整体姿势选择分别提高 5% 和 4.5%。Open-ComBind 将我们的基准数据集中配体的平均 RMSD 降低了 9.0%。我们在 www.github.com/drewnutt/open_combind 上提供 Open-ComBind 作为易于使用的命令行和 Python API，以提高姿势预测性能。