Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

中文翻译：

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Cobdock：一种准确实用的基于机器学习的共识盲对接方法

探测蛋白质表面以预测给定小分子的结合位点和结合亲和力是药物发现中的一项关键但具有挑战性的任务。盲对接通过对从整个蛋白质表面随机采样的结合区域进行对接来解决这个问题。然而，与本地对接相比，盲对接由于对接空间采样过大，准确性和可靠性较差。空腔检测引导的盲对接方法通过使用空腔检测（也称为结合位点检测）工具来指导对接过程，从而提高了准确性。然而，值得注意的是，这些方法的性能在很大程度上依赖于空腔检测工具的质量。这种约束，即对单个空腔检测工具的依赖，显着影响空腔检测引导方法的整体性能。为了克服这一限制，我们提出了共识盲对接（CoBDock），这是一种新颖的盲并行对接方法，它使用机器学习算法来集成对接和空腔检测结果，不仅提高了结合位点识别，而且提高了姿态预测的准确性。我们在 PDBBind 2020、ADS、MTi、DUD-E 和 CASF-2016 等多个数据集上的实验表明，CoBDock 比其他最先进的空腔检测器工具和盲对接方法具有更好的结合位点和结合模式性能。