  The discovery of drug-target interactions (DTIs) is a pivotal process in
pharmaceutical development. Computational approaches are a promising and
efficient alternative to tedious and costly wet-lab experiments for predicting
novel DTIs from numerous candidates. Recently, with the availability of
abundant heterogeneous biological information from diverse data sources,
computational methods have been able to leverage multiple drug and target
similarities to boost the performance of DTI prediction. Similarity integration
is an effective and flexible strategy to extract crucial information across
complementary similarity views, providing a compressed input for any
similarity-based DTI prediction model. However, existing similarity integration
methods filter and fuse similarities from a global perspective, neglecting the
utility of similarity views for each drug and target. In this study, we propose
a Fine-Grained Selective similarity integration approach, called FGS, which
employs a local interaction consistency-based weight matrix to capture and
exploit the importance of similarities at a finer granularity in both
similarity selection and combination steps. We evaluate FGS on five DTI
prediction datasets under various prediction settings. Experimental results
show that our method not only outperforms similarity integration competitors
with comparable computational costs, but also achieves better prediction
performance than state-of-the-art DTI prediction approaches by collaborating
with conventional base models. Furthermore, case studies on the analysis of
similarity weights and on the verification of novel predictions confirm the
practical ability of FGS.
