Supplements

Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources

 

Datasets:

      Chemical data source: Drug and PubChem chemical substructures relationships

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It includes 122,022 associations between 1007 drugs and 881 PubChem chemical substructures.

The descriptions of the 881 chemical substructures found here.

Generated from fingerprints of PubChem1.

      Protein data source: Drug and UniProt target proteins relationships

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It includes 3,152 associations between 1007 drugs and 775 target proteins.

Generated from targets of DrugBank2.

      Side-effect data source: Drug and SIDER side-effect keywords relationships

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It includes 61,102 associations between 888 drugs and 1385 side-effect terms.

Generated from SIDER3.

      Drug-disease treatment data source: Drug and therapeutic indication relationships

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It includes 3,250 treatment relationships between 799 drugs and 719 diseases.

Generated from Li and Lu4.

Results:

      Predicted drug-disease associations by our method

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It includes 3,870 predicted drug-disease associations by our method.

References:

1.    Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(Web Server Issue):W623-W633.

2.    Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(Database Issue):D901-D906.

3.    Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Molecular Systems Biology 2010;6:343.

4.    Li J, Lu Z. A New Method for Computational Drug Repositioning Using Drug Pairwise Similarity. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine 2012.