• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2017, Volume: 10, Issue: 13, Pages: 1-8

Original Article

Computational Regression and Prediction Analysis on a Dataset of 52 Pyrrolo[2,3-b]Pyridines and Pyrimidines Rheumatoid Arthritis Inhibitors


Objectives: To evaluate the dependency of physico-chemical properties of a set of BTK inhibitors on activity by linear regression analysis. Methods/Statistical Analysis: A multivariate regression analysis was implemented on 52 Pyrrolo[2,3-b]pyridines and pyrimidines which are reported as Bruton’s Tyrosine Kinase(BTK) inhibitors to construct a regression model using complete data set in the relationship between dependent variable and independent variable was estimated using python based regression analysis. Leverages were calculated in order to exclude outlying data from analysis Findings: A linear regression analysis on a complete dataset of BTK inhibitors as dependent variables and few independent variables resulted in F-test: 4.87, r value: 0.737 and r2 value of 0.543, respectively. The dataset investigated for the existence of outliers resulted in 50 BTK inhibitors after excluding 4q and 4zd from the dataset which resulted in improved r2 of 0.701 with better statistics. Further 44 compound training set resulted in improved r and r2 coefficients such as r: 0.816 and r2: 0.666, respectively and applying on a 6 compound validation set which determines regression model reliability and significance. Application/Improvements: Application of regression model to screen novel compounds with decreased H-bond acceptors, logP and KAlpha2 followed by an increase in KAlpha3 and randic index would enhance inhibitory activity against BTK.

Keywords: Bruton’s Tyrosine Kinase, Computational Regression, Python, Pyrrolo[2,3-b]Pyridines, Pyrimidines, Rheumatoid Arthritis


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