Identifying Drug-Target Interactions with Decision Templates.
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.


METHODS
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.


CONCLUSIONS
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 users.
Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.|College of Computer Science, Xi'an Shiyou University, Xi'an, 710065, China.
Xiao-Ying
XY
Yan
Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
Shao-Wu
SW
Zhang
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D066264:Datasets as Topic; D003661:Decision Support Techniques; D055808:Drug Discovery / Q000379:methods; D063990:Gene Ontology; D008024:Ligands; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D012984:Software
D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins
Drug-target interaction; decision template; drug similarity; gene ontology; identification; pathway; target similarity