Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be considered in sufficient details. In this work, a systematic and computer-aided virtual screening framework based on the binding site selectivity to screen candidate drug molecules in terms of their ability to bind on selected sites is presented. The framework integrates two machine learning (ML)-based models to predict the binding potential and binding selectivity to specific binding sites that are important for virtual screening of drug molecules. The details of the ML-based models together with the work-flow of the computer-aided virtual screening methods and the efficient and consistent integration of related drug design tools are presented. The applicability of this virtual screening framework is illustrated through a case study involving the screening for drug molecules as inhibitors to block the binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin converting enzyme 2 (ACE2), which is the target protein. The case study results point to identification of new candidate inhibitors with better binding site selectivity than two known potential inhibitors, Nilotinib and SSAA09E2.

中文翻译：

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基于结合位点选择性的小分子药物发现虚拟筛选框架

基于结构的候选药物分子结合虚拟筛选是小分子药物发现中越来越受关注的主题。由于同一药物分子可能与靶蛋白上的不同结合位点结合，因此需要充分考虑结合位点选择性，即候选药物分子到达靶蛋白后与不同结合位点的结合倾向。在这项工作中，提出了一个基于结合位点选择性的系统和计算机辅助虚拟筛选框架，根据候选药物分子在选定位点上的结合能力来筛选候选药物分子。该框架集成了两个基于机器学习 (ML) 的模型，以预测对特定结合位点的结合潜力和结合选择性，这对于药物分子的虚拟筛选非常重要。介绍了基于机器学习的模型的细节、计算机辅助虚拟筛选方法的工作流程以及相关药物设计工具的高效一致的集成。通过一个案例研究说明了该虚拟筛选框架的适用性，该案例研究涉及筛选药物分子作为抑制剂，以阻止严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 与血管紧张素转换酶 2 (ACE2) 的结合，该酶是目标蛋白。案例研究结果表明，新的候选抑制剂的鉴定比两种已知的潜在抑制剂（尼罗替尼和 SSAA09E2）具有更好的结合位点选择性。