Pose Filter-based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists

Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism thus are potential therapeutics to prevent and treat non-alcoholic fatty liver disease. The low success rate of FXR agonists R&D and the side effects of the clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery had rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR- (hFXR-) specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of FRED docking plus Chemgauss4 scoring function. It turned out that hFXR-specific pose filters ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE as the ideal approach for FXR agonists discovery. By screening of the ...