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Int. J. Mol. Sci. 2015, 16(6), 13407-13426; doi:10.3390/ijms160613407

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

1
State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Bilolgy, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
2
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Tatyana Karabencheva-Christova
Received: 25 March 2015 / Revised: 1 June 2015 / Accepted: 1 June 2015 / Published: 11 June 2015
(This article belongs to the Collection Proteins and Protein-Ligand Interactions)
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Abstract

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. View Full-Text
Keywords: pharmacophore; combinatorial 3D-QSAR; virtual screening; FGFR1 inhibitors pharmacophore; combinatorial 3D-QSAR; virtual screening; FGFR1 inhibitors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, N.; Xu, Y.; Liu, X.; Wang, Y.; Peng, J.; Luo, X.; Zheng, M.; Chen, K.; Jiang, H. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors. Int. J. Mol. Sci. 2015, 16, 13407-13426.

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