Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.
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The study was supported by the National Natural Science Foundation of China (No. 21807049, 81803430), the Fundamental Research Funds for the Central Universities (JUSRP51703A), the University-Industry Cooperation Research Project in Jiangsu (No. BY2020432), and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (PPZY2015B146).
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Zhu, J., Jiang, Y., Jia, L. et al. A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ. Mol Divers 25, 1271–1282 (2021). https://doi.org/10.1007/s11030-021-10243-1
- PI3Kγ inhibitor
- Molecular docking
- Virtual screening
- Naïve Bayesian Classification
- Machine learning