Retrieving novel C5aR antagonists using a hybrid ligand-based virtual screening protocol based on SVM classification and pharmacophore models

C5aR antagonists have been thought as potential immune mediators in various inflammatory and autoimmune diseases, and discovery of C5aR antagonists has attracted much attention in recent years. The discovery of C5aR antagonists was usually achieved through high-throughput screening, which usually suffered a high cost and a low success rate. Currently, the fast developing computer-aided virtual screening (VS) methods provide economic and rapid approaches to the lead discovery. In this account, we proposed a hybrid ligand-based VS protocol that is based on support vector machine (SVM) classification and pharmacophore models for retrieving novel C5aR antagonists. Performance evaluation of this hybrid VS protocol in virtual screening against a large independent test set, T-CHEM, showed that the hybrid VS approach significantly increased the hit rate and enrichment factor compared with the individual SVM classification model-based VS and pharmacophore model-based VS, as well as molecular docking-based VS in that the receptor structure was created by homology modeling. The hybrid VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally, a total of 20 compounds were selected from the top ranking hits, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.