Prediction of Drug-Target Interaction with Graph Regularized Non-Negative Matrix Factorization

Abstract

Identification of drug-target Interactions (DTIs) is very important for drug discovery, which can help to find the new uses for an old drug or to discover the off-targets for a given drug. Currently, algorithms have difficulty in finding interactions for new drugs and new targets. We proposed a novel method that uses graph regularized nonnegative matrix factorization framework to predict potential targets/drugs for new drugs/targets by using clustering approaches to construct interaction profiles for new drugs/targets. Compared with other methods, our method obtained the best performance in terms of AUPR.