Optimizing Area Under the Curve Measures via Matrix Factorization for Drug-Target Interaction Prediction

In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process. Area under the precision-recall curve (AUPR) that emphasizes the accuracy of top-ranked pairs and area under the receiver operating characteristic curve (AUC) that heavily punishes the existence of low ranked interacting pairs are two widely used evaluation metrics in the DTI prediction task. However, the two metrics are seldom considered as losses within existing DTI prediction methods. This paper proposes two matrix factorization methods that optimize AUPR and AUC, respectively. The two methods utilize graph regularization to ensure the local invariance of training drugs and targets in the latent feature space, and leverage the optimal decay coefficient to infer more reliable latent features of new drugs and targets. Experimental results over four updated benchmark datasets containing more recently verified interactions show the superiority of the proposed methods in terms of the corresponding evaluation metric they optimize.