  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. Although it has been shown that fusing heterogeneous drug
and target similarities can improve the prediction ability, the existing
similarity combination methods ignore the interaction consistency for neighbour
entities which is more crucial for the DTI prediction model. Furthermore, 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 DTI prediction. However, the two metrics
are seldom considered as losses within existing DTI prediction methods. This
paper first proposes two matrix factorization (MF) methods that optimize AUPR
and AUC using convex surrogate losses respectively, and then develops an
ensemble MF approach takes advantage of the two area under the curve metrics by
combining the two single metric based MF models. Both three proposed approaches
incorporate a novel local interaction consistency aware similarity interaction
method to generate fused drug and target similarities that preserve vital
information from the more reliable view. Experimental results over five
datasets under different prediction settings show that the proposed methods
outperform various competitors in terms of the metric(s) they optimize. In
addition, the validation on the top ranked novel predictions confirms the
ability of our methods to discover potential new DTIs.
