Computational discovery of novel PI3KC2α inhibitors using structure-based pharmacophore modeling, machine learning and molecular dynamic simulation

Abstract

PI3KC2α is a lipid kinase associated with cancer metastasis and thrombosis. In this study, we present a novel computational workflow integrating structure-based pharmacophore modeling, machine learning (ML), and molecular dynamics (MD) simulations to discover new PI3KC2α inhibitors. Key innovations include the generation of diverse pharmacophores from both crystallographic and docking-derived complexes, coupled with data augmentation via ligand conformational sampling to enhance ML robustness. The optimal model, developed using XGBoost with genetic function algorithm (GFA) and Shapley additive explanations (SHAP), identified four critical pharmacophores and three descriptors governing bioactivity. Virtual screening of the NCI database using these pharmacophores yielded three hits, with H_1 (NCI: 725847) demonstrating MD-derived binding stability and affinity comparable to the potent inhibitor PITCOIN1 (IC₅₀ = 95 nM). This study represents the first application of a conformation-augmented ML framework to PI3KC2α inhibition, offering a blueprint for targeting underexplored kinases with limited structural data.