Regulative Role of Atomic Auto Correlated Electronegativities and Polarizabilities in β2 Potency of Ultralong Acting Agonists Identified in QSAR Studies

Author(s): Srinivas Bandaru, Vinod Cingeetham, Uday Raj Akare, Deeksha Yadav, Nihit Aggarwal, Venkata Ravi Gutlapalli, Anuraj Nayarisseri, Mukesh Yadav

Journal Name: Current Bioinformatics

Volume 10 , Issue 5 , 2015

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QSAR models supervised by Multiple linear regressions (MLR) and Gaussian kernel support vector machines were developed to predict β2 potency for Sibenadet (Viozan™) and its derivatives along with established LABAs (Formeterol, Salmetrol) and ultra LABA Indacaterol. MLR aided linear QSAR models identified four molecular descriptors MATS6e, GATS5e, Mor17p, R7m+ related to β2 potency while descriptors like R5p+, Lop, Belp4, RDF075m were deduced in prediction of β2 potency in non-linear SVM models. Although, statistical fitness was observed with Gaussian Kernel function SVM models in potency prediction, MLR models proved to be more consistent in predictions. Further MLR and SVM models were statistically validated by internal validation methods like R2CV, RSS and MSS etc. Mechanistic study on linear QSAR models revealed regulative role of atomic autocorrelated electronegativities and polarizabilities in influencing β2 potency.

Keywords: Ultra long acting β2 agonists (uLABA), multiple linear regressions (MLR), support vector machine (SVM), linear and non-linear QSAR models.

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Article Details

Year: 2015
Published on: 13 November, 2015
Page: [503 - 508]
Pages: 6
DOI: 10.2174/1574893610666151007215927
Price: $65

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