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Nicotine and a variety of other drugs and toxins are metabolized by cytochromeP450 (CYP) 2A6. The aim of the present study was to build a quantitative structure-activityrelationship (QSAR) model to predict the activities of nicotine analogues on CYP2A6.Kernel partial least squares (K-PLS) regression was employed with the electro-topologicaldescriptors to build the computational models. Both the internal and external predictabilitiesof the models were evaluated with test sets to ensure their validity and reliability. As acomparison to K-PLS, a standard PLS algorithm was also applied on the same training andtest sets. Our results show that the K-PLS produced reasonable results that outperformed thePLS model on the datasets. The obtained K-PLS model will be helpful for the design ofnovel nicotine-like selective CYP2A6 inhibitors

An In Silico Method for Screening Nicotine Derivatives as Cytochrome P450 2A6 Selective Inhibitors Based on Kernel Partial Least Squares