DiffDock-Site: A Novel Paradigm for Enhanced Protein-Ligand Predictions through Binding Site Identification
Keywords: Molecular docking, Binding site identification, Geometry deep learning, Diffusion model
TL;DR: A novel paradigm that use a two-stage model to improve the efficiency of molecular docking.
Abstract: In the realm of computational drug discovery, molecular docking and ligand-binding site (LBS) identification stand as pivotal contributors, often influencing the direction of innovative drug development. DiffDock, a state-of-the-art method, is renowned for its molecular docking capabilities harnessing diffusion mechanisms. However, its computational demands, arising from its extensive score model designed to cater to a broad dynamic range for denoising score matching, can be challenging. To address this problem, we present DiffDock-Site, a novel paradigm that integrates the precision of PointSite for identifying and initializing the docking pocket. This two-stage strategy then refines the ligand's position, orientation, and rotatable bonds using a more concise score model than traditional DiffDock. By emphasizing the dynamic range around the pinpointed pocket center, our approach dramatically elevates both efficiency and accuracy in molecular docking. We achieve a substantial reduction in mean RMSD and centroid distance, from 7.5 to 5.2 and 5.5 to 2.9, respectively. Remarkably, our approach delivers these precision gains using only 1/6 of the model parameters and expends just 1/13 of the training time, underscoring its unmatched combination of computational efficiency and predictive accuracy.
Submission Number: 9
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