Assessing the binding affinity of a selected class of DPP4 inhibitors using chemical descriptor-based multiple linear regression

The activity of a selected class of DPP4 inhibitors was preliminarily assessed using chemical descriptors derived AM1 optimized geometries. Using multiple linear regression model, it was found that Δ[E.sup.0], LUMO energy, area, molecular weight and Δ[H.sup.0] are the significant descriptors that ca...

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Published inOrbital : The Electronic Journal of Chemistry Vol. 3; no. 1; pp. 15 - 23
Main Authors Janairo, Jose Isagani B, Janairo, Gerardo C, Co, Frumencio F, Yu, Derrick Ethelbhert C
Format Journal Article
LanguageEnglish
Published Universidade Federal de Mato Grosso do Sul 01.01.2011
Subjects
am1
Analysis
Blood sugar
chemical descriptors
Chemical properties
dpp4 inhibitors
Glucose metabolism
multiple linear regression
Regression analysis
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Summary:The activity of a selected class of DPP4 inhibitors was preliminarily assessed using chemical descriptors derived AM1 optimized geometries. Using multiple linear regression model, it was found that Δ[E.sup.0], LUMO energy, area, molecular weight and Δ[H.sup.0] are the significant descriptors that can adequately assess the binding affinity of the compounds. The derived multiple linear regression (MLR) model was validated using rigorous statistical analysis. The preliminary model suggests that bulky and electro-philic inhibitors are desired. Keywords: DPP4 inhibitors; AM1; chemical descriptors; multiple linear regression
ISSN:1984-6428
1984-6428
DOI:10.17807/orbital.v3i1.206