Predicting inhibitor for multidrug resistance associated protein-2 transporter

(Bentham Science Publishers) The aim of the present work was to develop a machine learning predictive model to classify inhibitors and non-inhibitors of multidrug resistance associated protein-2 transporter using a well refined dataset.In this study, the various algorithms of machine learning were used to develop the predictive models i.e. support vector machine, random forest and k-nearest neighbor. The methods like variance threshold, SelectKBest, random forest, and recursive feature elimination were used to select the features generated by PyDPI.