构建首个系统性共价药物设计基准，推动靶向药物研发
CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions
- 将共价对接分解为三个任务：反应位点预测、反应路径预测与对接
- 基于Uni-Mol等模型建立基线，准确预测结合位点与分子转化过程
- 适合药物研发人员与计算化学学者，助力选择性共价抑制剂发现
分子对接在预测配体与靶蛋白结合模式中起关键作用，而涉及配体与靶点形成共价键的共价相互作用因其强且持久的结合特性尤为珍贵。然而，现有对接方法和深度学习模型普遍忽略共价键形成及伴随的结构变化。为此，我们提出一个全面的共价对接基准CovDocker，旨在更好捕捉共价结合的复杂性。我们将共价对接过程拆分为三个主要任务：反应位点预测、共价反应预测与共价对接。通过适配Uni-Mol和Chemformer等前沿模型，建立基线性能，并验证该基准在准确预测相互作用位点及建模共价结合中分子转化过程的有效性。结果表明，该基准可作为推进共价药物设计研究的严格框架，凸显数据驱动方法加速选择性共价抑制剂发现的潜力，解决治疗开发中的关键挑战。
原文摘要 · Abstract (English)
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.
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