Abstract
The present study deals with successful utilization of both correlation and classification techniques for the development of diverse models for the prediction of dual mTOR and PI3Kα inhibitory activities using a dataset comprising of 39 analogues of 4-morpholinopyrrolopyrimidines. Decision tree, random forest, moving average analysis (MAA) and multiple linear regression (MLR) were used to develop models for mTOR and PI3Kα inhibitory activities. The proposed models were also found to be sensitive towards the cell inhibition against PC3. The models were assessed for statistical significance in terms of overall accuracy of prediction, specificity, sensitivity, Mathew’s correlation coefficient (MCC) and intercorrelation analysis. The high predictability of the proposed models of diverse nature offers enormous potential for providing lead molecules for the development of potent medicinal agents for dual mTOR and PI3Kα inhibition.
Keywords: mTOR inhibitors and PI3Kα inhibitors, 4-morpholinopyrrolopyrimidines, Topological descriptors, Decision Tree, Random forest, Moving average analysis.
Letters in Drug Design & Discovery
Title:Diverse Models for the Prediction of Dual mTOR and PI3Kα Inhibitory Activities of Substituted 4-morpholinopyrrolopyrimidines
Volume: 11 Issue: 4
Author(s): Monika Gupta and A. K. Madan
Affiliation:
Keywords: mTOR inhibitors and PI3Kα inhibitors, 4-morpholinopyrrolopyrimidines, Topological descriptors, Decision Tree, Random forest, Moving average analysis.
Abstract: The present study deals with successful utilization of both correlation and classification techniques for the development of diverse models for the prediction of dual mTOR and PI3Kα inhibitory activities using a dataset comprising of 39 analogues of 4-morpholinopyrrolopyrimidines. Decision tree, random forest, moving average analysis (MAA) and multiple linear regression (MLR) were used to develop models for mTOR and PI3Kα inhibitory activities. The proposed models were also found to be sensitive towards the cell inhibition against PC3. The models were assessed for statistical significance in terms of overall accuracy of prediction, specificity, sensitivity, Mathew’s correlation coefficient (MCC) and intercorrelation analysis. The high predictability of the proposed models of diverse nature offers enormous potential for providing lead molecules for the development of potent medicinal agents for dual mTOR and PI3Kα inhibition.
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Cite this article as:
Gupta Monika and Madan K. A., Diverse Models for the Prediction of Dual mTOR and PI3Kα Inhibitory Activities of Substituted 4-morpholinopyrrolopyrimidines, Letters in Drug Design & Discovery 2014; 11 (4) . https://dx.doi.org/10.2174/15701808113106660089
DOI https://dx.doi.org/10.2174/15701808113106660089 |
Print ISSN 1570-1808 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-628X |
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