Decoding the limits of deep learning in molecular docking for drug discovery.

ABSTRACT: Structure-based molecular docking, a cornerstone of computational drug design, is undergoing a paradigm shift fueled by deep learning (DL) innovations. However, the rapid proliferation of DL-driven docking methods has created uncharted challenges in translating in silico predictions to biomedical reality. Here, we delve into the performance and prospects of traditional methods and state-of-the-art DL docking paradigms-encompassing generative diffusion models, regression-based architectures, and hybrid frameworks-across five critical dimensions: pose prediction accuracy, physical plausibility, interaction recovery, virtual screening (VS) efficacy, and generalization across diverse protein-ligand landscapes. We reveal that generative diffusion models achieve superior pose accuracy, wh

SUBMITTER: Li Y 

PROVIDER: S-EPMC12401186 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature