Navigating the chemical space of dipeptidyl peptidase-4 inhibitors

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.

Comparative Binding Analysis of Dipeptidyl Peptidase IV (DPP-4) with Antidiabetic Drugs – An Ab Initio Fragment Molecular Orbital Study

“…So, this superior activity of 4 should have come from other quarters. It has been concluded in previous studies [10, 43] that interaction at S 2 extensive subsite by 1 and 4 contributes to increase in their inhibitory activities. Among these two inhibitors, 4 has a larger contact area (0.92 nm 2 ) in the S 2 extensive subsite than 1 has (0.71 nm 2 ) and, it has been concluded by Nabeno et al [10] that the former may bind more tightly to this extensive subsite and this might be one of the reasons for its larger inhibitory activity.…”

ASHA:A Chemical Sapce Navigator app for Dual–Target-Directed-Inhibitor design

“…I In recent years, researchers have used these properties to explore and compare the chemical diversity and space of databases like NuBBEDB, the first Brazilian biodiversity library of natural products (Saldívar-González et al, 2019) and food chemical databases (Naveja et al, 2018). Exploratory data analysis for large-scale chemical space exploration and DPP4 inhibitor QSAR uses physicochemical properties (Shoombuatong et al, 2015). Descriptive statistics of physicochemical properties were used to study the structural diversity and chemical space of DNA methyltransferases inhibitors, which are more polar than clinical trial drugs and molecules (Gortari and Medina-Franco, 2015).…”

Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis

“…A total of 1,149,497 published papers were assembled in the Web of Science Core Collection (WoSCC) until June 2023 on the topics of "diabetes", 930,263 of which have been published after the year 2000 The scientific community's interest in the management of this disease has grown considerably (about 40%), observing a significant increase in scientific publications from approximately 17,000 in the year 2000 to approximately 70,000 in the year 2022 (Figure 1). So, in the early stage of drug design, in silico requirements have been managed by using various computational approaches, such as pharmacophore modeling [28][29][30][31], quantitative structure-activity relationships (QSAR) [32][33][34][35], molecular docking [36][37][38][39], molecular dynamics simulation [40][41][42], DFT simulation [35,[43][44][45][46], etc. These techniques have generated notable interest by reducing the time required for experimental trials, as well as human and resource costs.…”