Semi-supervised Drug-Protein Interaction Prediction from Heterogeneous Spaces∗

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interaction while myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often gives better results than using the labeled data alone. Further, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. We report encouraging results of our method on drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.