Publication: Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection
Issued Date
2014-04-09
Resource Type
ISSN
17683254
02235234
02235234
Other identifier(s)
2-s2.0-84896804902
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
European Journal of Medicinal Chemistry. Vol.76, (2014), 352-359
Suggested Citation
Chanin Nantasenamat, Teerawat Monnor, Apilak Worachartcheewan, Prasit Mandi, Chartchalerm Isarankura-Na-Ayudhya, Virapong Prachayasittikul Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection. European Journal of Medicinal Chemistry. Vol.76, (2014), 352-359. doi:10.1016/j.ejmech.2014.02.043 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/33624
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection
Other Contributor(s)
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.