  Modeling the interaction between proteins and ligands and accurately
predicting their binding structures is a critical yet challenging task in drug
discovery. Recent advancements in deep learning have shown promise in
addressing this challenge, with sampling-based and regression-based methods
emerging as two prominent approaches. However, these methods have notable
limitations. Sampling-based methods often suffer from low efficiency due to the
need for generating multiple candidate structures for selection. On the other
hand, regression-based methods offer fast predictions but may experience
decreased accuracy. Additionally, the variation in protein sizes often requires
external modules for selecting suitable binding pockets, further impacting
efficiency. In this work, we propose $\mathbf{FABind}$, an end-to-end model
that combines pocket prediction and docking to achieve accurate and fast
protein-ligand binding. $\mathbf{FABind}$ incorporates a unique ligand-informed
pocket prediction module, which is also leveraged for docking pose estimation.
The model further enhances the docking process by incrementally integrating the
predicted pocket to optimize protein-ligand binding, reducing discrepancies
between training and inference. Through extensive experiments on benchmark
datasets, our proposed $\mathbf{FABind}$ demonstrates strong advantages in
terms of effectiveness and efficiency compared to existing methods. Our code is
available at https://github.com/QizhiPei/FABind

Machine Learning;Artificial Intelligence;Biomolecules