Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach

Background: The efflux transporter multidrug resistance associated protein-2 belongs to ATP-binding cassette superfamily which plays an important role in multidrug resistance and drug-drug interactions. Efflux transporters are considered to be important targets for increasing the efficacy of drugs and importance of computational study of efflux transporters for predicting substrates, non-substrates, inhibitors and non-inhibitors is well documented. Previous work on predictive models for inhibitors of multidrug resistance associated Protein-2 efflux transporter showed that machine learning methods produced good results. Objective: 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. Method: 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. A total of 239 molecules consisting of 124 inhibitors and 115 non-inhibitors were used for model development Results: The best multidrug resistance associated protein-2 inhibitor model showed prediction accuracies of 0.76, 0.72 and 0.79 for training, 5-fold cross-validation and external sets, respectively. Conclusion: It was observed that support vector machine model built on features selected using recursive feature elimination method shows the best performance. The developed model can be used in the early stages of drug discovery for identifying the inhibitors of multidrug resistance associated protein-2 efflux transporter.