Discovering new PI3Kα inhibitors with a strategy of combining ligand-based and structure-based virtual screening

PI3K alpha is a promising drug target for cancer chemotherapy. In this paper, we report a strategy of combing ligand-based and structure-based virtual screening to identify new PI3K alpha inhibitors. First, na < ve Bayesian (NB) learning models and a 3D-QSAR pharmacophore model were built based upon known PI3K alpha inhibitors. Then, the SPECS library was screened by the best NB model. This resulted in virtual hits, which were validated by matching the structures against the pharmacophore models. The pharmacophore matched hits were then docked into PI3K alpha crystal structures to form ligand-receptor complexes, which are further validated by the Glide-XP program to result in structural validated hits. The structural validated hits were examined by PI3K alpha inhibitory assay. With this screening protocol, ten PI3K alpha inhibitors with new scaffolds were discovered with IC50 values ranging 0.44-31.25 mu M. The binding affinities for the most active compounds 33 and 74 were estimated through molecular dynamics simulations and MM-PBSA analyses. [GRAPHICS] .