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

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Abstract

PI3Kα 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 PI3Kα inhibitors. First, naïve Bayesian (NB) learning models and a 3D-QSAR pharmacophore model were built based upon known PI3Kα 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 PI3Kα 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 PI3Kα inhibitory assay. With this screening protocol, ten PI3Kα inhibitors with new scaffolds were discovered with IC50 values ranging 0.44–31.25 μM. The binding affinities for the most active compounds 33 and 74 were estimated through molecular dynamics simulations and MM-PBSA analyses.

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Acknowledgements

This work was supported by National Science Foundation of China (Nos. 81473138, 81171575, 81271805; 81371793, 81530069), GD Frontier & KeyTechn. Innovation Program (2015B010109004), GD-NSF (2016A030310228). GD Key Lab. Construction Foundation (2011A060901014), Collaborative Innovation Center of HPC, NUDT, Changsha.

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  1. Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China

    Miao Yu, Qiong Gu & Jun Xu

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  1. Miao Yu
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  2. Qiong Gu
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  3. Jun Xu
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The experiment design JX, MY, QG. Implementation: MY. Manuscript revision and submission: MY and JX.

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Correspondence to Jun Xu.

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Yu, M., Gu, Q. & Xu, J. Discovering new PI3Kα inhibitors with a strategy of combining ligand-based and structure-based virtual screening. J Comput Aided Mol Des 32, 347–361 (2018). https://doi.org/10.1007/s10822-017-0092-8

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