Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.

Abstract

PI3Kγ is a lipid kinase that is expressed primarily in leukocytes and plays a significant role in tumors, inflammation, and autoimmune diseases. Consequently, considerable attention has been given to the development of pharmacological inhibitors of PI3Kγ. Recently, machine learning-based virtual screening approaches have been increasingly applied in new drug discovery research, potentially providing innovative strategies for the development of PI3Kγ inhibitors. Thus, in this study, we developed a naïve Bayesian classification (NBC) model that integrates molecular descriptors, molecular fingerprints, molecular docking, and pharmacophore models for virtual screening of the PI3Kγ protein. The validation results indicated that the optimal model demonstrated significant potential for differentiating between active and inactive compounds, as well as a reliable ability to identify true PI3Kγ inhibitors with defined biological activity. Additionally, the optimal NBC model provided favorable and unfavorable fragments for PI3Kγ inhibitors, which will help guide the design and discovery of novel PI3Kγ inhibitors. Finally, the optimal NBC model was employed to perform virtual screening on the ChEMBL database, resulting in the identification of several compounds with high potential as PI3Kγ inhibitors. We anticipate that the developed machine learning-based virtual screening approach will offer valuable insights and guidance for the development of novel PI3Kγ inhibitors.