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Open Access Highly Accessed Research article

Drug repositioning: a machine-learning approach through data integration

Francesco Napolitano12, Yan Zhao3, Vânia M Moreira4, Roberto Tagliaferri1, Juha Kere5, Mauro D’Amato5 and Dario Greco35*

Author Affiliations

1 Department of Computer Science, University of Salerno, Salerno, Italy

2 Telethon Institute of Genetics and Medicine (TIGEM), Naples, Italy

3 Research Unit of Molecular Medicine, University of Helsinki, Helsinki, Finland

4 Division of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland

5 Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden

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Journal of Cheminformatics 2013, 5:30  doi:10.1186/1758-2946-5-30

Published: 22 June 2013

Abstract

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Keywords:
Drug repositioning; Connectivity map; CMap; ATC code; Mode of action; Machine learning; SVM; Integrative genomics; SMILES; Anthelmintics; Antineoplastic; Oxamniquine; Niclosamide