Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To address this limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless integration with docking workflows. When guiding AutoDock Vina, RAPID-Net outperforms DiffBindFR on the PoseBusters benchmark and enables blind docking on large proteins that AlphaFold 3 cannot process as a whole. Furthermore, RAPID-Net surpasses PUResNet and Kalasanty in docking accuracy and pocket-ligand intersection rates across diverse datasets, including PoseBusters, Astex Diverse Set, BU48, and Coach420. When accuracy is evaluated as ``at least one correct pose in the ensemble'', RAPID-Net outperforms AlphaFold 3 on the PoseBusters benchmark, suggesting that our approach can be further improved with a suitable pose reweighting tool offering a cost-effective and competitive alternative to AlphaFold 3 for docking. Finally, using several therapeutically relevant examples, we demonstrate the ability of RAPID-Net to identify remote functional sites, highlighting its potential to facilitate the development of innovative therapeutics.

翻译：精确识别可成药口袋对于基于结构的药物设计至关重要。然而，大多数口袋识别算法优先考虑其几何特性，而非下游对接性能。为解决这一局限，我们开发了RAPID-Net，这是一种可与对接工作流程无缝集成的口袋发现算法。在指导AutoDock Vina时，RAPID-Net在PoseBusters基准测试中优于DiffBindFR，并能够对AlphaFold 3无法整体处理的大蛋白质进行盲对接。此外，在包括PoseBusters、Astex Diverse Set、BU48和Coach420在内的多种数据集上，RAPID-Net在对接精度和口袋-配体交集率方面均超越了PUResNet和Kalasanty。当以"集合中至少有一个正确构象"作为精度评估标准时，RAPID-Net在PoseBusters基准测试中优于AlphaFold 3，这表明我们的方法可以通过合适的构象重加权工具进一步改进，为对接提供一种经济高效且具有竞争力的AlphaFold 3替代方案。最后，通过几个治疗相关的实例，我们展示了RAPID-Net识别远端功能位点的能力，突显了其促进创新疗法开发的潜力。