GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

ABSTRACT: Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformatio

SUBMITTER: Guo L 

PROVIDER: S-EPMC11070652 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature