Journal of Computer-Aided Molecular Design

, Volume 32, Issue 2, pp 347–361 | Cite as

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

Article
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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.

Graphical Abstract

Keywords

PI3Kα inhibitor Machine learning 3D-QSAR pharmacophore Virtual screening 

Notes

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.

Author contributions

The experiment design JX, MY, QG. Implementation: MY. Manuscript revision and submission: MY and JX.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.

Supplementary material

10822_2017_92_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1139 KB)
10822_2017_92_MOESM2_ESM.xlsx (270 kb)
Supplementary material 2 (XLSX 269 KB)
10822_2017_92_MOESM3_ESM.xlsx (117 kb)
Supplementary material 3 (XLSX 116 KB)

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Center for Drug Discovery, School of Pharmaceutical SciencesSun Yat-Sen UniversityGuangzhouChina

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