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An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei

An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei

Ranita Pal, Goutam Pal, Gourhari Jana, Pratim Kumar Chattaraj
Copyright: © 2019 |Volume: 8 |Issue: 2 |Pages: 12
ISSN: 2155-4110|EISSN: 2155-4129|EISBN13: 9781522567097|DOI: 10.4018/IJCCE.20190701.oa1
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MLA

Pal, Ranita, et al. "An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei." IJCCE vol.8, no.2 2019: pp.57-68. http://doi.org/10.4018/IJCCE.20190701.oa1

APA

Pal, R., Pal, G., Jana, G., & Chattaraj, P. K. (2019). An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei. International Journal of Chemoinformatics and Chemical Engineering (IJCCE), 8(2), 57-68. http://doi.org/10.4018/IJCCE.20190701.oa1

Chicago

Pal, Ranita, et al. "An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei," International Journal of Chemoinformatics and Chemical Engineering (IJCCE) 8, no.2: 57-68. http://doi.org/10.4018/IJCCE.20190701.oa1

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Abstract

Human African trypanosomiasis (HAT) is a vector-borne sleeping sickness parasitic disease spread through the bite of infected tsetse flies (Glossina genus), which is highly populated in rural Africa. The present study constructed quantitative structure-activity relationship (QSAR) models based on quantum chemical electronic descriptors to bring out the extent to which the electronic factor of the selected compounds affects the HAT activity. Theoretical prediction of toxicity (pIC50) of the series of heterocyclic scaffolds consisting 32 pyridyl benzamide derivatives towards HAT is investigated by considering all possible combinations of electrophilicity index (ω) and the square of electrophilicity index (ω2) as descriptors in the studied models along with other descriptors previously used by Masand et al. A multiple linear regression (MLR) analysis is conducted to develop the models. Further, in order to obtain the variable selection on the overall data set having diverse functional groups, the analysis using sum of ranking differences methodology with ties is carried out.