Identification of Potential Dual Farnesol X Receptor/Retinoid X Receptor α Agonists Based on Machine Learning Models, ADMET Prediction and Molecular Docking

Abstract

AbstractNuclear receptors (NRs) play a crucial role in the pathogenesis of metabolic syndrome. Farnesol X receptor (FXR) and retinoid X receptor (RXR) are members of the NR superfamily and are usually present as heterodimers in vivo. Screening for multi‐target NR activators is of great importance due to the complex pathogenesis of metabolic diseases. Virtual screening is often used for drug discovery. In this study, we first collected data on relevant compounds and subsequently constructed three machine learning models (Random Forest, Support Vector Machine, and Artificial Neural Network) with molecular descriptors. We then performed model fusion based on the soft voting strategy and the prediction accuracies of the fused models were 85.2 % for the FXR external validation set and 84.3 % for the RXRα external validation set, followed by virtual screening of compounds in the ZINC database according to a model score threshold of 0.7. The 499 hits commonly selected by both models were then subjected to a drug‐likeness filter and ADMET prediction using appropriate bioavailability parameters. The 10 compounds that met the criteria were processed by molecular docking using the AutoDock Vina software. The results showed that we screened four potential dual FXR/RXRα agonists with binding energies less than −8.0 kcal/mol for both targets (ZINC1557163, ZINC14824986, ZINC5273978 and ZINC40394465).