Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

• Chemical space and QSAR modeling was performed on a set of sulfonylpyridazinones.
• Monte Carlo feature selection revealed 5 important descriptors.
• Computational study sheds light on origins of aldose reductase inhibitory activity.
• Performance of QSAR models was verified by internal and external validation.

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.

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