Identification of PI3K alpha inhibitors through large-scale virtual screening and integrated molecular modeling, biophysical characterization, and ADMET profiling

Lung cancer remains one of the leading causes of cancer-associated mortality globally, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. The PI3K p110α catalytic subunit (PIK3CA) signaling pathway is often dysregulated in non-small cell lung cancer (NSCLC) and plays a vital role in promoting tumor cell proliferation, survival, and resistance to apoptosis, making it a clinically validated therapeutic target. Despite the approval of several PI3K p110α inhibitors, challenges including off-target toxicity, drug resistance, and suboptimal pharmacokinetic profiles require the continued discovery of novel, drug-like candidates. In this study, virtual screening of 650,000 compounds targeting the PI3K p110α pathway identified five lead candidates based on the lowest docking binding energy. MD simulations over 100 ns, MM-GBSA/MM-PBSA binding free energy calculations were performed using 1000 frames extracted at 100 ps intervals from the 100 ns molecular dynamics simulation trajectory. The results showed negative binding free energy values for all complexes, computationally indicating favorable binding interactions and potential stability of the compounds with the target protein. DFT analysis and SwissADME ADMET profiling were performed. All five complexes showed stable RMSD profiles and consistently negative binding free energies. DFT confirmed acceptable HOMO–LUMO energy gaps. ADMET profiling demonstrated satisfactory drug-likeness for all candidates. Five promising PI3K p110α inhibitor candidates with potential anti-lung cancer activity were identified; experimental validation is required to confirm these computational results.