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 (GCN) and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrodinger Dock with an AUC of 0.72, accuracy of 0.68, and TPR of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and RMSE of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with 3 (F545-1970, K297-0698, S948-0241) out of 8 candidates. Furthermore, we also successfully obtained 6 active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.