3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

Jiang, Chaoyi; Han, Shuang; Chen, Tiegang; Chen, Jianzhong

doi:10.1016/j.apsb.2012.06.007

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…Then they have synthesized several derivatives and measured their IC 50 Value 19,20 or performed virtual screening from a large database [20][21][22][23] . In most of the cases they have used a structurally similar scaffold for QSAR modeling and that's too in less number [24][25][26] . But in this cumulative study, we have collected 180 highly diversified molecules with their experimental PIC50 value.…”

Section: Discussionmentioning

confidence: 99%

Pharmacoinformatics Profiling and Dynamic Studies of Selected Compounds Acting as Potential Inhibitors against DPP4 Enzyme

Roy

Bhowmik²,

Sengupta³

et al. 2021

Orient. J. Chem

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DPP-IV rapidly degrades glucagon-like peptide-1 and glucose-dependent insulinotropic peptides. Delaying the breakdown of endogenous incretin hormones with DPP-IV inhibitors may help correct the physiologic deficit. The purpose of this work is to identify new compounds that inhibit the DPP-IV enzyme. The anticipated compounds were potent anti-diabetic candidates in this investigation. Two 2d QSAR models were created using 179 different substances from diverse sources. QSAR models were created using two methods. The first technique included docking score as an additional descriptor, while the second did not. Docking-based QSAR considered 74 compounds out of 179. Another approach used 40 molecules from 179 compounds. Each method had a precise strategy. Descriptors were computed using DRAGON for both training and test sets. Using DRAGON data, SYSTAT generated regression curves. The docking-based QSAR model produced R2=0.7098 (training set) and R2=0.9987 (test set), whereas the other technique produced R2=0.7644 (training set) and R2=0.9857 (test set).

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Section: Discussionmentioning

confidence: 99%

Pharmacoinformatics Profiling and Dynamic Studies of Selected Compounds Acting as Potential Inhibitors against DPP4 Enzyme

Roy

Bhowmik²,

Sengupta³

et al. 2021

Orient. J. Chem

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“…In this study, 41 DPP-IV inhibitors were collected from different literature sources [7,15,16,17,18,19,20,21,22]. Two-dimensional structures of the compounds were generated using the Accelrys Draw4.1 program and were saved as mol files.…”

Section: Methodsmentioning

confidence: 99%

Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes Mellitus Predicted by 3D QSAR Pharmacophore Models, Molecular Docking and De Novo Evolution

et al. 2019

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Dipeptidyl peptidase-IV (DPP-IV) rapidly breaks down the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). Thus, the use of DPP-IV inhibitors to retard the degradation of endogenous GLP-1 is a possible mode of therapy correcting the defect in incretin-related physiology. The aim of this study is to find a new small molecule and explore the inhibition activity to the DPP-IV enzyme using a computer aided simulation. In this study, the predicted compounds were suggested as potent anti-diabetic candidates. Chosen structures were applied following computational strategies: The generation of the three-dimensional quantitative structure-activity relationship (3D QSAR) pharmacophore models, virtual screening, molecular docking, and de novo Evolution. The method also validated by performing re-docking and cross-docking studies of seven protein systems for which crystal structures were available for all bound ligands. The molecular docking experiments of predicted compounds within the binding pocket of DPP-IV were conducted. By using 25 training set inhibitors, ten pharmacophore models were generated, among which hypo1 was the best pharmacophore model with the best predictive power on account of the highest cost difference (352.03), the lowest root mean squared deviation (RMSD) (2.234), and the best correlation coefficient (0.925). Hypo1 pharmacophore model was used for virtual screening. A total of 161 compounds including 120 from the databases, 25 from the training set, 16 from the test set were selected for molecular docking. Analyzing the amino acid residues of the ligand-receptor interaction, it can be concluded that Arg125, Glu205, Glu206, Tyr547, Tyr662, and Tyr666 are the main amino acid residues. The last step in this study was de novo Evolution that generated 11 novel compounds. The derivative dpp4_45_Evo_1 by all scores CDOCKER_ENERGY (CDOCKER, -41.79), LigScore1 (LScore1, 5.86), LigScore2 (LScore2, 7.07), PLP1 (-112.01), PLP2 (-105.77), PMF (-162.5)—have exceeded the control compound. Thus the most active compound among 11 derivative compounds is dpp4_45_Evo_1. Additionally, for derivatives dpp4_42_Evo_1, dpp4_43_Evo2, dpp4_46_Evo_4, and dpp4_47_Evo_2, significant upward shifts were recorded. The consensus score for the derivatives of dpp4_45_Evo_1 from 1 to 6, dpp4_43_Evo2 from 4 to 6, dpp4_46_Evo_4 from 1 to 6, and dpp4_47_Evo_2 from 0 to 6 were increased. Generally, predicted candidates can act as potent occurring DPP-IV inhibitors given their ability to bind directly to the active sites of DPP-IV. Our result described that the 6 re-docked and 27 cross-docked protein-ligand complexes showed RMSD values of less than 2 Å. Further investigation will result in the development of novel and potential antidiabetic drugs.

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“…Their model is useful for designing new DPP IV inhibitors. In [14], Jiang et al developed a QSAR model for a set of arymethylamines as a DPP IV inhibitor by using a CoMFA approach with r 2 0.953. In [15] [16] analyzed 45 derivatives of triazolopiperazime amida as DPP IV inhibitors by using a 3D-QSAR that shows values of r 2 is 0.868 for both CoMFA and CoMSIA approaches, and r 2 pred are 0.816 and 0.863 for CoMFA and CoMSIA, respectively, for their best model.…”

Section: Introductionmentioning

confidence: 99%

DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Septiawan

Prakoso

Kurniawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.

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3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

Cited by 10 publications

References 20 publications

Pharmacoinformatics Profiling and Dynamic Studies of Selected Compounds Acting as Potential Inhibitors against DPP4 Enzyme

Pharmacoinformatics Profiling and Dynamic Studies of Selected Compounds Acting as Potential Inhibitors against DPP4 Enzyme

Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes Mellitus Predicted by 3D QSAR Pharmacophore Models, Molecular Docking and De Novo Evolution

DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

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