CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.

ABSTRACT: The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep l

SUBMITTER: Cai H 

PROVIDER: S-EPMC10806797 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature