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Network Modeling Analysis in Health Informatics and Bioinformatics

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01.12.2016 | Original Article

Computational design of novel renin inhibitors of indole-3-carboxamide derivatives through QSAR studies

verfasst von: Mukesh C. Sharma

Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2016

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Abstract

A series of 47 indole-3-carboxamide derivatives with their renin inhibitors were subjected to QSAR to derive a correlation between biological activity as a dependent variable and various descriptors as independent variables. The best 2D-QSAR model having cross-validated squared correlation coefficient q ² = 0.7847 with external predictive of pred_r ² = 0.8193. Based on k-nearest neighbor method the results some key structural factors responsible for renin inhibitors of this series of compounds were revealed as follows: the substituent R1 should have higher steric potential is favorable and substituent R2, and R3 position electron donating group may be favorable. The structural insights gleaned from the study could be usefully employed to design activators with a much more enhanced potency.

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Titel: Computational design of novel renin inhibitors of indole-3-carboxamide derivatives through QSAR studies
verfasst von: Mukesh C. Sharma
Publikationsdatum: 01.12.2016
Verlag: Springer Vienna
Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics / Ausgabe 1/2016
Print ISSN: 2192-6662
Elektronische ISSN: 2192-6670
DOI: https://doi.org/10.1007/s13721-016-0116-7

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