Abstract
TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure-activity relationship (QSAR) models for three bioactive endpoints (K(i), IC(50), and EC(50)) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the K(i) endpoint, the Bagging-ECFP4 model had a Q(2) value of 0.778 and an R(2) value of 0.780. For the IC(50) endpoint, the XGBoost-ECFP4 model had a Q(2) value of 0.806 and an R(2) value of 0.784. For the EC(50) endpoint, the SVM-Daylight model had a Q(2) value of 0.784 and an R(2) value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints.