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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/33624
Title: Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection
Authors: Chanin Nantasenamat
Teerawat Monnor
Apilak Worachartcheewan
Prasit Mandi
Chartchalerm Isarankura-Na-Ayudhya
Virapong Prachayasittikul
Mahidol University
Keywords: Chemistry;Medicine;Pharmacology, Toxicology and Pharmaceutics
Issue Date: 9-Apr-2014
Citation: European Journal of Medicinal Chemistry. Vol.76, (2014), 352-359
Abstract: This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR). Predictive QSAR models were then constructed by MLR, support vector machine and artificial neural network, which afforded good predictive performance as deduced from internal and external validation. The investigated models are capable of accounting for the origins of aldose reductase inhibitory activity and could be utilized in predicting this property in screening for novel and robust compounds. © 2014 Elsevier Masson SAS. All rights reserved.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84896804902&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/33624
ISSN: 17683254
02235234
Appears in Collections:Scopus 2011-2015

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