Duc-Hau Le1, Ly Le. 1. School of Computer Science and Engineering, Water Resources University, 175 Tay Son, Dong Da, Hanoi, Vietnam. hauldhut@gmail.com.
Abstract
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.
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.