QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method

Kumar, Ashwani; Chauhan, Shilpi

doi:10.1055/s-0042-119725

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…We have also developed differential QSAR models with dataset consisting of same 45 SIRT1 inhibitors along with 67 SIRT1 activators using same methodology . These QSAR models led to extraction of molecular features essential for differentiation between SIRT1 activators and inhibitors.…”

Section: Resultsmentioning

confidence: 99%

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Kumar

Chauhan²

2016

Archiv der Pharmazie

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SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R = 0.8350, Q = 0.7491 for the test set and R = 0.9655, Q = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes.

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Section: Resultsmentioning

confidence: 99%

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Kumar

Chauhan²

2016

Archiv der Pharmazie

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“…Along with this, molecular graph generated from it are also of three types such as hydrogen suppressed graph (HSG), hydrogen filled graph (HFG) and graph of atomic orbitals (GAO) [26]. In this work, graph based descriptors of Correlation Weights (DCW) were used to determine the molecular features [27] of compounds. T and N epoch are the factors employed in Monte Carlo method where T is the threshold and N epoch is described as the number of the epochs of the Monte Carlo optimization.…”

Section: Optimal Descriptorsmentioning

confidence: 99%

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

et al. 2020

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Fructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

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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: 96%

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 Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method

Cited by 17 publications

References 31 publications

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

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

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