dc.contributor.authorPatra, Jagdish Chandra
dc.contributor.authorSingh, Onkar
dc.date.accessioned2011-09-22T04:28:23Z
dc.date.available2011-09-22T04:28:23Z
dc.date.copyright2009en_US
dc.date.issued2009
dc.identifier.citationPatra, 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.en_US
dc.identifier.issn0192-8651en_US
dc.identifier.urihttp://hdl.handle.net/10220/7106
dc.description.abstractIn 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.en_US
dc.format.extent15 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of computational chemistryen_US
dc.rights© 2009 Wiley Periodicals, Inc.en_US
dc.subjectDRNTU::Science::Chemistry
dc.subjectDRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
dc.titleArtificial neural networks-based approach to design ARIs using QSAR for diabetes mellitusen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1002/jcc.21240
dc.identifier.rims140324


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