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

Kumar, Ashwani; Chauhan, Shilpi

doi:10.1002/ardp.201600268

Cited by 24 publications

(10 citation statements)

References 58 publications

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“…The major purpose behind QSAR modeling is to achieve a model which can predict the property of the designed molecules in an appropriate way. The developed QSAR models were validated by various statistical techniques suggested in literature [31][32][33][34][35].…”

Section: Validation Of Qsar Modelmentioning

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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Section: Validation Of Qsar Modelmentioning

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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“…In the field of QSAR, we considered several aspects to promote the applicability to regulatory requests. Such key points go beyond the classical Organisation for Economic Co‐operation and Development (OECD) principles, which are progressively more and more accounted for in published QSAR works (e.g., Castillo‐Garit et al ; Kumar and Chauhan ; Nendza et al ). To further improve the acceptance of QSAR models at the regulatory level, in our opinion, several aspects need to be kept in mind when developing and publishing a new model, as described in the list below.…”

Section: Recommendations and Needs For Researchmentioning

confidence: 99%

Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability—Remarks and research needs

Vighi

Barsi²,

Focks

et al. 2019

Integr Envir Assess & Manag

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This paper concludes a special series of 7 articles (4 on toxicokinetic–toxicodynamic [TK–TD] models and 3 on quantitative structure‐activity relationship [QSAR] models) published in previous issues of Integrated Environmental Assessment and Management (IEAM). The present paper summarizes the special series articles and highlights their contribution to the topic of increasing the regulatory applicability of effect models. For both TK–TD and QSAR approaches, we then describe the main research needs. The use of TK–TD models for describing sublethal effects must be better developed, particularly through the improvement of the dynamic energy budget (DEBtox) approach. The potential of TK–TD models for moving from lower (molecular) to higher (population) hierarchical levels is highlighted as a promising research line. Some relevant issues to improve the acceptance of QSAR models at the regulatory level are also described, such as increased transparency of the performance assessment and of the modeling algorithms, model documentation, relevance of the chosen target for regulatory needs, and improved mechanistic interpretability. Integr Environ Assess Manag 2019;00:000–000. © 2019 SETAC

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“…We have also developed regression based QSAR models on dataset of 45 SIRT1 inhibitors using the same software and methodology [51]. These models are quantitative in nature and the values of response variable are continuous and numerical.…”

Section: The Confusion Matrix Of Each Differential Model Is Shown In mentioning

confidence: 99%

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

Kumar

Chauhan

2016

Drug Res (Stuttg)

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Silent information regulator 2 homologue one (SIRT1) modulators have therapeutic potential for a number of diseases like cardiovascular, metabolic, inflammatory and age related disorders. Here, we have studied both activators and inhibitors of SIRT1 and constructed differential quantitative structure activity relationship (QSAR) models using CORAL software by Monte Carlo optimization method and SMILES notation. 3 splits divided into 3 subsets: sub-training, calibration and test sets, were examined and validated with a prediction set. All the described models were statistically significant models. The values of sensitivity, specificity, accuracy and Matthews' correlation coefficient for the validation set of best model were 1.0000, 0.8889, 0.9524 and 0.9058, respectively. In mechanistic interpretation, structural features important for SIRT1 activation and inhibition have been defined.

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

Cited by 24 publications

References 58 publications

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

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

Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability—Remarks and research needs

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

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