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G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure–activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR–ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR–ligand complexes. As a result, we developed GPCR–ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist. [ABSTRACT FROM AUTHOR]

In the present report, 3D-QSAR analysis was executed on the previously synthesized and evaluated derivatives of isoquinolin-1-ones and quinazolin-4-ones; potent inhibitors of tumor necrosis factor α (TNFα). Statistically significant 3D-QSAR models were generated using 42 molecules in the training set. The predictive ability of models was determined using a randomly chosen test set of 16 molecules, which gave excellent predictive correlation coefficients for 3-D models, suggesting good predictive index. Pharmacophore prediction generated a five point pharmacophore (AAHRR): two hydrogen bond acceptor (A), one hydrophobic (H) and two ring (RR) features. This pharmacophore hypothesis furnished a statistically meaningful 3D-QSAR model with partial least-square (PLS) factors seven having R2=0.9965, Q2=0.6185, Root Mean Squared Error=0.4284 and Pearson-R=0.853. Docking study revealed the important amino acid residues (His 15, Tyr 59, Tyr 151, Gly 121 and Gly 122) in the active site of TNFα that are involved in binding of the active ligand. Orientation of the pharmacophore hypothesis AAHRR.25 corresponded very closely with the binding mode recorded in the active site of ligand bound complex. The results of ligand based pharmacophore hypothesis and atom based 3D-QSAR furnished crucial structural insights and also highlighted the important binding features of isoquinolin-1-ones and quinazolin-4-ones derivatives, which may provide guidance for the rational design of novel and more potent TNFα inhibitors.