In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches but is fundamentally limited by the accuracy with which protein-ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase-inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures cocrystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the cocrystallized ligand, utilizing shape overlap with or without maximum common substructure matching, are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance of generating a low root-mean-square deviation (RMSD) docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar cocrystallized ligands according to the maximum common substructure (MCS) proved to be the most efficient way to reproduce binding poses, achieving a success rate of 70.4% across all included systems. The studied docking and pose selection strategies, which utilize the OpenEye Toolkits, were implemented into pipelines of the KinoML framework, allowing automated and reliable protein-ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe that the general findings can also be transferred to other protein families.

中文翻译：

---

激酶药物发现中的基准交叉转运策略。


近年来，机器学习已经改变了药物发现过程的许多方面，包括小分子设计，其中生物活性的预测是不可或缺的一部分。利用有关小分子与其蛋白质靶标之间相互作用的结构信息对于下游机器学习评分方法具有巨大潜力，但从根本上受到以可靠和自动化方式预测蛋白质-配体复杂结构的准确性的限制。为了找到为下游机器学习评分方法生成有用的激酶抑制剂复合物几何形状的实用方法，我们提出了一个以激酶为中心的对接基准，评估不同类别的对接和姿势选择策略的性能，以评估在现实交叉会诊场景中对实验观察到的结合模式的概括程度。组装的基准数据集侧重于经过充分研究的蛋白激酶家族，包括 589 种蛋白质结构的子集，这些蛋白质结构与 423 种 ATP 竞争性配体共结晶。我们发现，由共结晶配体偏置的对接方法，利用形状重叠，有或没有最大公共子结构匹配，比单独使用基于物理的标准对接更成功地恢复结合姿势。此外，停靠到多个结构中会显著增加生成低均方根偏差 （RMSD） 停靠姿势的几率。根据最大公共子结构 （MCS） 利用将所有三种方法 （Posit） 结合到具有最相似共结晶配体的结构中的方法进行对接，被证明是重现结合姿势的最有效方法，在所有包含的系统中实现了 70.4% 的成功率。 利用 OpenEye 工具包研究的停靠和姿势选择策略已实施到 KinoML 框架的管道中，允许为未来的下游机器学习任务自动生成可靠的蛋白质-配体复合物。虽然专注于蛋白激酶，但我们相信一般发现也可以转移到其他蛋白质家族。