FlowDock：面向生成式蛋白质-配体对接与亲和力预测的几何流匹配
FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
AI总结：
本研究提出基于条件流匹配的深度几何生成模型FlowDock，可同时实现多配体柔性蛋白质-配体对接、结构置信度与亲和力预测，在多个基准测试中表现优异，助力药物研发中的虚拟筛选。
摘要：
近期已有多种强大的蛋白质-配体结构生成式AI模型被提出，但这些方法中很少能同时支持柔性蛋白质-配体对接和亲和力估计。在同时具备这两项功能的方法中，没有一种能够直接同时对多个结合配体建模，也未在药理学相关的药物靶点上经过严格基准测试，阻碍了它们在药物研发工作中的广泛应用。本研究中，我们提出FlowDock，这是首个基于条件流匹配的深度几何生成模型，能够学习将未结合（apo，载脂蛋白态）结构直接映射到对应任意数量结合配体的已结合（holo，全配体态）结构。此外，FlowDock会为每个生成的蛋白质-配体复合物结构提供预测的结构置信度分数和结合亲和力值，可实现对新型（多配体）药物靶点的快速虚拟筛选。在知名的PoseBusters Benchmark数据集上，FlowDock使用未结合（apo）蛋白质输入结构且不借助任何来自多序列比对的信息，以51%的盲对接成功率优于单序列AlphaFold 3；而在具有挑战性的新数据集DockGen-E上，FlowDock的表现优于单序列AlphaFold 3，且在结合口袋泛化能力上与单序列Chai-1相当。此外，在第16届社区级结构预测技术关键评估（CASP16）的配体类别中，FlowDock在针对140个蛋白质-配体复合物的药理学结合亲和力评估任务中跻身前五，证明了其学习到的表征在虚拟筛选中的有效性。源代码、数据和预训练模型可在https://github.com/BioinfoMachineLearning/FlowDock获取。
英文摘要：
Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands. Furthermore, FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures, enabling fast virtual screening of new (multi-ligand) drug targets. For the well-known PoseBusters Benchmark dataset, FlowDock outperforms single-sequence AlphaFold 3 with a 51% blind docking success rate using unbound (apo) protein input structures and without any information derived from multiple sequence alignments, and for the challenging new DockGen-E dataset, FlowDock outperforms single-sequence AlphaFold 3 and matches single-sequence Chai-1 for binding pocket generalization. Additionally, in the ligand category of the 16th community-wide Critical Assessment of Techniques for Structure Prediction (CASP16), FlowDock ranked among the top-5 methods for pharmacological binding affinity estimation across 140 protein-ligand complexes, demonstrating the efficacy of its learned representations in virtual screening. Source code, data, and pre-trained models are available at https://github.com/BioinfoMachineLearning/FlowDock.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
