Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

ABSTRACT: G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while pr

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11167311 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature