title: Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection.
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.
authors.affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand. Electronic address: chanin.nan@mahidol.ac.th.
authors.forename: Chanin
authors.initials: C
authors.lastname: Nantasenamat
authors.affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Teerawat
authors.initials: T
authors.lastname: Monnor
authors.affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Apilak
authors.initials: A
authors.lastname: Worachartcheewan
authors.affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Prasit
authors.initials: P
authors.lastname: Mandi
authors.affiliation: Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Chartchalerm
authors.initials: C
authors.lastname: Isarankura-Na-Ayudhya
authors.affiliation: Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Virapong
authors.initials: V
authors.lastname: Prachayasittikul
mesh_terms: D000449:Aldehyde Reductase / Q000037:antagonists & inhibitors*; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D009010:Monte Carlo Method; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D004791:Enzyme Inhibitors; D000449:Aldehyde Reductase
keywords: Aldose reductase; Aldose reductase inhibitor; MC-MLR; Monte Carlo; QSAR