Title |
Design of a tripartite network for the prediction of drug targets
|
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Published in |
Perspectives in Drug Discovery and Design, January 2018
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DOI | 10.1007/s10822-018-0098-x |
Pubmed ID | |
Authors |
Ryo Kunimoto, Jürgen Bajorath |
Abstract |
Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 4 | 20% |
Student > Postgraduate | 3 | 15% |
Researcher | 3 | 15% |
Student > Ph. D. Student | 3 | 15% |
Student > Doctoral Student | 1 | 5% |
Other | 3 | 15% |
Unknown | 3 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 4 | 20% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 15% |
Biochemistry, Genetics and Molecular Biology | 2 | 10% |
Agricultural and Biological Sciences | 2 | 10% |
Computer Science | 2 | 10% |
Other | 4 | 20% |
Unknown | 3 | 15% |