LAUSR

Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is of great importance. Drug-target interaction prediction is especially relevant in the case of drug repositioning where attempts are made to repurpose old drugs for new indications. While experimental wet-lab techniques exist for predicting such interactions, they are tedious and time-consuming. On the other hand, computational methods also exist for predicting interactions, and they do so with reasonable accuracy. In addition, computational methods can help guide their wet-lab counterparts by recommending interactions for further validation. In this chapter, a computational method for predicting drug-target interactions is presented. Specifically, we describe a machine learning method that utilizes ensemble learning to perform predictions. We also mention details pertaining to the preparation of the data required for the prediction effort and demonstrate how to evaluate and improve prediction performance.