Background: Drug discovery is one important issue in medicine and pharmacology area. Traditional
methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached
in a system-level view and therefore it is called systems pharmacology. This research field deals with the
problems in drug discovery by integrating various kinds of biomedical and pharmacological data and using advanced
computational methods. Ultimately, the problems are more effectively solved. One of the most important
problem in systems pharmacology is prediction of drug-target interactions. Methods: In this review, we are going
to summarize various computational methods for this problem. Results: More importantly, we formed a unified
framework for the problem. In addition, to study human health and disease in a more systematically and effectively,
we also presented an integrated scheme for a wider problem of prediction of disease-gene-drug associations.
Conclusion: By presenting the unified framework and the integrated scheme, underlying computational methods for problems in systems
pharmacology can be understood and complex relationships among diseases, genes and drugs can be identified effectively.
Keywords: Drug-target interaction, network-based approach, machine learning-based approach, drug-disease association, disease-gene
association, drug-gene-disease association.
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