Development and application of novel molecular descriptors for predicting biological activity View Full Text


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Article Info

DATE

2017-05-06

AUTHORS

R. Dutt, A. K. Madan

ABSTRACT

Three new topostructural indices termed as superpendentic distance sum indices 1–3, as well as their topochemical versions have been conceptualized in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed indices were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of G protein-coupled receptor-40 agonistic activity of phenylpropanoic acids. A wide variety of two-dimensional and three-dimensional molecular descriptors including proposed indices were employed for decision tree analysis. The values of majority of these descriptors for each analog in the data set were computed using E-Dragon software (version 1.0). An in-house computer program was also employed to calculate additional molecular descriptors which were not included in E-Dragon software. The decision tree learned the information from the input data with an accuracy of 97% and correctly predicted the cross-validated (10 fold) data with accuracy up to 70%. Three non-correlating descriptors of diverse nature were subsequently utilized for development of suitable models using moving average analysis. These models predicted G protein-coupled receptor-40 agonistic activity with an accuracy ranging from 91–97%. The statistical significance of models/indices was assessed through intercorrelation analysis, sensitivity, specificity, and Matthew’s correlation coefficient. High sensitivity towards branching, high discriminating power amalgamated with negligible degeneracy offer proposed indices a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design. More... »

PAGES

1988-2006

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00044-017-1906-x

DOI

http://dx.doi.org/10.1007/s00044-017-1906-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085180186


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