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BioMed Research International
Volume 2015 (2015), Article ID 584546, 14 pages
http://dx.doi.org/10.1155/2015/584546
Research Article

Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

1Institute of Systems Biology, Shanghai University, Shanghai 200444, China
2Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China
3State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
4Department of Mathematics, East China Normal University, Shanghai 200241, China

Received 9 August 2014; Accepted 11 September 2014

Academic Editor: Tao Huang

Copyright © 2015 Guohua Huang 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

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.