Background: During the development process of new drugs, identification of the drug-target
interactions wins primary concerns. However, the chemical or biological experiments bear the limitation
in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity,
chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large
scale and have no luxurious need about target structures or ligand entries.
Objective: In order to reflect the cases that the drugs having variant structures interact with common
targets and the targets having dissimilar sequences interact with same drugs. In addition, though several
other similarity metrics have been developed to predict DTIs, the combination of multiple similarity
metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities.
Method: In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target
similarity metrics to address above issues. More importantly, we propose a more effective strategy via
decision template to integrate multiple classifiers designed with multiple similarity metrics.
Results: In the scenarios that predict existing targets for new drugs and predict approved drugs for new
protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are
able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple
classifiers has better predictive power than the naïve combination of multiple similarity metrics.
Conclusion: Compared with other two state-of-the-art approaches on the four popular benchmark
datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and
AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein
targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions.
The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic