FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction

Accurate prediction of protein–ligand complexes and binding affinity is critical for hit identification and optimization for structure-based drug design. Traditional docking simulates binding processes with searching algorithms guided by energy-scoring functions, which are quite computationally expe...

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Автори:	Jing Li, Ruiqiang Lu, Yi Tan, Pengyu Liang, Bo Liu, Shukai Gu, Huanxiang Liu, Xiaojun Yao
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Elsevier 2026-07-01
Серія:	Acta Pharmaceutica Sinica B
Предмети:	Structure-based drug design Protein–ligand docking Binding affinity prediction Machine learning in drug discovery Bayesian flow networks Deep equivariant generative models
Онлайн доступ:	http://www.sciencedirect.com/science/article/pii/S2211383526002492
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FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction

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