标题： CovDocker：通过任务、数据集和解决方案进行共价药物设计的基准测试 显示英文标题

标题： CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions

摘要： 分子对接在预测配体与靶蛋白的结合模式中起着关键作用，共价相互作用由于其强而持久的结合特性，涉及配体与靶蛋白之间形成共价键，尤其有价值。 然而，大多数现有的对接方法和深度学习方法几乎不考虑共价键的形成及其相关的结构变化。 为解决这一差距，我们引入了一个全面的共价对接基准，CovDocker，旨在更好地捕捉共价结合的复杂性。 我们将共价对接过程分解为三个主要任务：反应位点预测、共价反应预测和共价对接。 通过适应最先进的模型，如Uni-Mol和Chemformer，我们建立了基线性能，并证明了该基准在准确预测相互作用位点和建模共价结合中涉及的分子转变方面的有效性。 这些结果证实了该基准作为推进共价药物设计研究的严格框架的作用。 它强调了数据驱动方法在加速选择性共价抑制剂的发现以及解决治疗开发中的关键挑战方面的潜力。 显示英文摘要

摘要： Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development.