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Artificial neural network-based drug design for diabetes mellitus using flavonoids

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Artificial neural network-based drug design for diabetes mellitus using flavonoids

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dc.contributor.author Patra, Jagdish Chandra
dc.contributor.author Chua, Boon H.
dc.date.accessioned 2011-10-12T08:56:00Z
dc.date.available 2011-10-12T08:56:00Z
dc.date.copyright 2010
dc.date.issued 2011-10-12
dc.identifier.citation Patra, J. C., & Chua, B. H. (2010). Artificial Neural Network-based Drug Design for Diabetes Mellitus Using Flavonoids. Journal of Computational Chemistry, 32, 555-567.
dc.identifier.issn 0192-8651
dc.identifier.uri http://hdl.handle.net/10220/7251
dc.description.abstract Diabetes mellitus is a chronic metabolic disease involving the failure to regulate glucose blood levels in the body and has been linked with numerous detrimental complications. Studies have shown that these complications can be linked to the activities of aldose reductase (AR), an enzyme of the polyol pathway. Flavonoids have been identified as good AR inhibitors (ARIs) and are also strong antioxidants with radical scavenging (RS) activity. As such, flavonoids show potential to become a better class of ARIs because they are able to concurrently address the oxidative stress issue. In this article, we carried out quantitative structure-activity relationship analysis of flavones and flavonols (members of flavonoid family) using artificial neural networks. Three computer experiments were conducted to study the influence of hydrogen (H), hydroxyl (OH), and methoxyl (CH3) functional groups on eight substitution sites of the lead flavone molecule and to predict potential ARIs. Of 6561 possible flavones and flavonols, in experiment 1, we predicted 69 potent ARIs, and in experiment 2, we predicted 346 compounds with strong RS activity. In experiment 3, we combined these results to find overlapping compounds with both strong AR inhibition and RS activity and we are able to predict 10 potent compounds with strong AR inhibition (IC50 < 0.3 μM) and RS activity (IC25 < 1.0 μM). These 10 compounds show promise of being good therapeutic agents in the prevention of diabetic complications and is suggested to undergo further wet bench experimentation to prove their potency.
dc.format.extent 13 p.
dc.language.iso en
dc.relation.ispartofseries Journal of computational chemistry
dc.rights © 2010 Wiley Periodicals, Inc.
dc.subject DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences.
dc.title Artificial neural network-based drug design for diabetes mellitus using flavonoids
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.doi http://dx.doi.org/10.1002/jcc.21641
dc.identifier.rims 155054

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