AI 中文总结
研究旨在为共价对接开发高效评分套件。核心方法是结合反应前对接、量子化学配体反应性描述符及口袋静电性质，用非线性树模型汇总描述符。主要贡献是BCover在COValid基准评估中表现良好，速度快且能恢复近天然配体构象，为共价配体优先级排序提供策略。
AI 中文摘要
共价虚拟筛选需要根据非共价识别以及化合物与具有适当反应性弹头采取反应活性几何结构的能力对化合物进行排名。在此，我们引入了BCover，这是一种反应感知评分套件，它将反应前对接与量子化学衍生的配体反应性描述符、蛋白质口袋的静电性质相结合。量子化学描述符使用基于非线性树的评分模型进行汇总。BCover在由九个靶点和十个反应位点组成的COValid基准上进行了回顾性评估，并与AutoDock、DOCK6、DOCKovalent、带有Rosetta重新评分的AlphaFold3以及基于AlphaFold3置信度的排名进行了比较。BCover的平均调整后LogAUC为31%（最大值为57%），平均ROC-AUC为0.88（最大值为0.96），平均EF1为18（最大值为33）。其平均LogAUC超过了经典对接方法和AlphaFold3-Rosetta，尽管AlphaFold3-mPAE提供了最强的总体富集。BCover平均每个配体的运行时间约为10秒，比评估的AlphaFold3工作流程快约25倍，并实现了最高的平均时间调整虚拟筛选生产力指数。重新对接实验进一步表明该方法恢复了接近天然配体构象。这些结果表明，将对接衍生的几何结构与配体局部电子反应性和口袋静电相结合，为共价配体优先级排序提供了一种高效且可解释的策略。BCover旨在作为一种高通量筛选方法，在后续的先导优化过程中补充计算要求更高的QM/MM和自由能计算。
英文摘要
Covalent virtual screening requires ranking compounds according to both noncovalent recognition and their ability to adopt a reaction-competent geometry with an appropriately reactive warhead. Here, we introduce BCover, a reaction-aware scoring suite that combines pre-reactive docking with quantum-chemistry-derived ligand reactivity descriptors, the electrostatic properties of the protein pocket. Quantum-chemical descriptors are aggregated using a nonlinear tree-based scoring model. BCover was evaluated retrospectively on the COValid benchmark, comprising nine targets and ten reactive sites, and compared with AutoDock, DOCK6, DOCKovalent, AlphaFold3 with Rosetta rescoring, and AlphaFold3 confidence-based ranking. BCover achieved an average adjusted LogAUC of 31% (57% max), an average ROC-AUC of 0.88 (0.96 max), an average EF1 of 18 (33 max). Its average LogAUC exceeded those of the classical docking methods and AlphaFold3-Rosetta, although AlphaFold3-mPAE provided the strongest overall enrichment. At an average runtime of about 10~s per ligand, BCover was approximately 25-fold faster than the evaluated AlphaFold3 workflows and achieved the highest average time-adjusted virtual-screening productivity index. Redocking experiments further showed that the method recovered near-native ligand conformations. These results demonstrate that combining docking-derived geometry with ligand local electronic reactivity and pocket electrostatics provides an efficient and interpretable strategy for covalent ligand prioritization. BCover is intended as a high-throughput screening method that complements more computationally demanding QM/MM and free-energy calculations during subsequent lead optimization.
