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Drug Res (Stuttg) 2017; 67(03): 156-162
DOI: 10.1055/s-0042-119725
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

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

Ashwani Kumar
1   Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
,
Shilpi Chauhan
1   Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
› Author Affiliations
Further Information

Publication History

received 10 August 2016

accepted 20 October 2016

Publication Date:
19 December 2016 (online)

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Abstract

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.

Supporting Information

 

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