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BioMed Research International
Volume 2013, Article ID 485034, 8 pages
http://dx.doi.org/10.1155/2013/485034
Research Article

Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
3Shanghai Center for Bioinformation Technology, Shanghai 200235, China
4Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, NY 10029, USA
5Department of Ophthalmology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
6State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai 201203, China
7Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China
8Institute of Systems Biology, Shanghai University, Shanghai 200444, China
9Gordon Life Science Institute, Belmont, Massachusetts 02478, USA
10Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia

Received 18 June 2013; Accepted 30 July 2013

Academic Editor: Bing Niu

Copyright © 2013 Lei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.