Drug side-effect prediction based on the integration of chemical and biological spaces

Drug side-effects, or adverse drug reactions, have become a major public health concern and remain one of the main causes of drug failure and of drug withdrawal once they have reached the market. Therefore, the identification of potential severe side-effects is a challenging issue. In this paper, we...

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Vydáno v:Journal of chemical information and modeling Ročník 52; číslo 12; s. 3284
Hlavní autoři: Yamanishi, Yoshihiro, Pauwels, Edouard, Kotera, Masaaki
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 21.12.2012
Témata:
Computational Biology - methods
Drug-Related Side Effects and Adverse Reactions
Models, Molecular
Protein Conformation
Proteins - chemistry
Proteins - metabolism
ISSN:1549-960X, 1549-960X
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Shrnutí:Drug side-effects, or adverse drug reactions, have become a major public health concern and remain one of the main causes of drug failure and of drug withdrawal once they have reached the market. Therefore, the identification of potential severe side-effects is a challenging issue. In this paper, we develop a new method to predict potential side-effect profiles of drug candidate molecules based on their chemical structures and target protein information on a large scale. We propose several extensions of kernel regression model for multiple responses to deal with heterogeneous data sources. The originality lies in the integration of the chemical space of drug chemical structures and the biological space of drug target proteins in a unified framework. As a result, we demonstrate the usefulness of the proposed method on the simultaneous prediction of 969 side-effects for approved drugs from their chemical substructure and target protein profiles and show that the prediction accuracy consistently improves owing to the proposed regression model and integration of chemical and biological information. We also conduct a comprehensive side-effect prediction for uncharacterized drug molecules stored in DrugBank and confirm interesting predictions using independent information sources. The proposed method is expected to be useful at many stages of the drug development process.
Bibliografie:ObjectType-Article-1
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ISSN:1549-960X
1549-960X
DOI:10.1021/ci2005548