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Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus

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Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus

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dc.contributor.author Patra, Jagdish Chandra
dc.contributor.author Singh, Onkar
dc.date.accessioned 2011-09-22T04:28:23Z
dc.date.available 2011-09-22T04:28:23Z
dc.date.copyright 2009
dc.date.issued 2011-09-22
dc.identifier.citation Patra, J. C., & Singh, O. (2009). Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus. Journal of Computational Chemistry, 30(15), 2494-2508.
dc.identifier.issn 0192-8651
dc.identifier.uri http://hdl.handle.net/10220/7106
dc.description.abstract In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead structure. We have shown that the multilayer perceptron-based model is capable of determining the QSAR quite satisfactorily, with high R-value. Usually, the design of potent ARIs requires the use of complex computer docking and quantum mechanical (QM) steps involving excessive time and human judgement. In the second part of this article, to reduce the design cycle of potent ARIs, we propose a novel ANN technique to eliminate the computer docking and QM steps, to predict the total score. The MLP-based QSAR models obtained in the first part are used to predict the potent ARIs, using the experimental data reported by Hu et al. (J Mol Graph Mod 2006, 24, 244). The proposed ANN-based model can predict the total score with an R-value of 0.88, which indicates that there exists a close match between the predicted and experimental total scores. Using the ANN model, we obtained 71 potent ARIs out of 6.25 million new ARI compounds created by substituting different functional groups at substituting sites of main lead structure of known ARI. Finally, using high bioactivity relationship and total score values, we determined four potential ARIs out of these 71 compounds. Interestingly, these four ARIs include the two potent ARIs reported by Hu et al. (J Mol Graph Mod 2006, 24, 244) who obtained these through the complex computer docking and QM steps. This fact indicates the effectiveness of our proposed ANN-based technique. We suggest these four compounds to be the most promising candidates for ARIs to prevent the diabetic complications and further recommend for wet bench experiments to find their potential against AR in vitro and in vivo.
dc.format.extent 15 p.
dc.language.iso en
dc.relation.ispartofseries Journal of computational chemistry
dc.rights © 2009 Wiley Periodicals, Inc.
dc.subject DRNTU::Science::Chemistry.
dc.subject DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences.
dc.title Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.doi http://dx.doi.org/10.1002/jcc.21240
dc.identifier.rims 140324

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