Drug-Target Interaction Prediction by Learning From Local Information and Neighbors

Motivation: In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, Bipartite Local Model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure “local” model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates.