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Database

Drug-Target Interactions database

Study of drug-target networks is an important topic toward the elucidation of protein functions and the understanding of the molecular mechanisms of action inside the cell. It is both time consuming and costly to determine ligand-protein interactions or potential drug-target interactions by experiments alone. In this study,three supervised inference methods, derived from recommendation algorithms of complex networks, were developed to predict new drug-target interactions and drug repositioning. They are drug-based similarity inference, target-based similarity inference and network-based inference (NBI). Excellent performance was obtained for these methods on four benchmark datasets. Among them, NBI performed better than the others. Via NBI some new drug-target interactions were predicted based on 12,483 FDA approved and experimental drug-target links. In vitro assays confirmed that five old drugs showed novel polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with IC50 values ranged from 200nM to 10μΜ. And two old drugs showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of drug–target interactions and drug repositioning.

Experimental Drug-Target Interactions database

Virtual Drug-Target Interactions database

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Reference

Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Weixing Zhou, Jin Huang, and Yun Tang. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference. To be submitted.