QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method

Sokolović, Dušan; Ranković, Jasmina; Stanković, Veroljub; Stefanović, Rade; Karaleić, Sladjan; Mekić, Branimir; Milenković, Vesko; Kocić, Jadranka; Veselinović, Aleksandar M.

doi:10.1007/s00044-017-1792-2

Cited by 24 publications

(4 citation statements)

References 44 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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“…As a result, these models can predict a specific chemical or physical characteristic of a chemical molecule. [30][31][32][33][34] CORAL (CORrelation And Logic) is a free QSAR/ASPR software program that can be used to create QSPR models for multiple endpoints using the inbuilt Monte Carlo technique. [35][36][37][38][39] CORAL employs SMILES (simplified molecular-input line-entry system) notations of chemical structures to compute the descriptor of correlation weight (DCW).…”

Section: Introductionmentioning

confidence: 99%

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Lotfi,

Ahmadi,

Azimi

et al. 2023

New J. Chem.

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“…The results are easy to interpret and can be applied in designing of new compounds [5][6][7]. Earlier, CORAL has been used for QSAR studies of various types of compounds like SIRT1 modulators [8,9], anti-neuraminidase agents [10], anticonvulsant agents [11], antimalarial agents [12], NNRTI inhibitors [13], antibacterial agents [14], anticancer agents [15], aromatase inhibitors [16], antihuman serine proteinases [17], acetylcholinesterase inhibitors [18,19], peptidase-4 inhibitors [20], glycogen synthase kinase-3β inhibitors [21], and lipase inhibitors [22] etc.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles

Kumar

2017

Drug Res (Stuttg)

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Monte Carlo method based QSAR studies for inhibitors of Mer kinase, a potential novel target for cancer treatment, has been carried out using balance of correlation technique. The data was divided into three random and dissimilar splits and hybrid optimal descriptors derived from SMILES and hydrogen filled graphs based notations were used for construction of QSAR models. The generated models have good fitting ability, robustness, generalizability and internal predictive ability. The external predictive ability has been tested using multiple criteria and described models exhibited good performance in all of these tests. The values of R, Q, R, Q, R and ∆R for the best model are 0.9502, 0.9388, 0.9469, 0.9083, 0.7534 and 0.0894 respectively. Also, the structural characteristics responsible for enhancement and reduction of activity have been extracted. Further, the agreement with the OECD rules for QSAR model has been discussed.

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QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method

Cited by 24 publications

References 44 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

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles

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