G protein-coupled receptors (GPCRs) are the most frequently addressed drug targets in the pharmaceutical industry. However, achieving highly safety and efficacy in designing of GPCR drugs is quite challenging since their primary amino acid sequences show fairly high homology. Systematic study on the interaction spectra of inhibitors with multiple human GPCRs will shed light on how to design the inhibitors for different diseases which are related to GPCRs. To reach this goal, several proteochemometric models were constructed based on different combinations of two protein descriptors, two ligand descriptors and one ligand–receptor cross-term by two kinds of statistical learning techniques. Our results show that support vector regression (SVR) performs better than Gaussian processes (GP) for most combinations of descriptors. The transmembrane (TM) identity descriptors have more powerful ability than the z-scale descriptors in the characterization of GPCRs. Furthermore, the performance of our PCM models was not improved by introducing the cross-terms. Finally, based on the TM Identity descriptors and 28-dimensional drug-like index, two best PCM models with GP and SVR (GP-S-DLI: R²=0.9345, Q²_test=0.7441; SVR-S-DLI: R²=1.0000, Q²_test=0.7423) were derived respectively. The area of ROC curve was 0.8940 when an external test set was used, which indicates that our PCM model obtained a powerful capability for predicting new interactions between GPCRs and ligands. Our results indicate that the derived best model has a high predictive ability for human GPCR–inhibitor interactions. It can be potentially used to discover novel multi-target or specific inhibitors of GPCRs with higher efficacy and fewer side effects.