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Navigating the chemical space of dipeptidyl peptidase-4 inhibitors

Authors Shoombuatong W, Prachayasittikul V, Anuwongcharoen N, Songtawee N, Monnor T, Prachayasittikul S, Prachayasittikul V, Nantasenamat C

Received 13 April 2015

Accepted for publication 3 June 2015

Published 10 August 2015 Volume 2015:9 Pages 4515—4549

DOI https://doi.org/10.2147/DDDT.S86529

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Professor Wei Duan

Watshara Shoombuatong,1 Veda Prachayasittikul,1,2 Nuttapat Anuwongcharoen,1 Napat Songtawee,1 Teerawat Monnor,1 Supaluk Prachayasittikul,1 Virapong Prachayasittikul,2 Chanin Nantasenamat1,2

1Center of Data Mining and Biomedical Informatics, 2Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand

Abstract: This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 µM and greater than 10 µM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes.

Keywords: QSAR, decision tree, scaffold analysis, fragment analysis, antidiabetic, molecular docking, rational drug design

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