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Issue 11, 2015
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Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

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Abstract

Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process. Identification of features critical for specifying side effects would facilitate efficient computational procedures for their prediction. We devised a generalized ordinary canonical correlation model for prediction of drug side effects based on their chemical properties as well as their target profiles. While the former is based on 2D and 3D chemical features, the latter enumerates a systems-level property of drugs. We find that the model incorporating chemical features outperforms that incorporating target profiles. Furthermore we identified the 2D and 3D chemical properties that yield best results, thereby implying their relevance in specifying adverse drug reactions.

Graphical abstract: Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

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Publication details

The article was received on 04 May 2015, accepted on 29 Jul 2015 and first published on 29 Jul 2015


Article type: Paper
DOI: 10.1039/C5MB00312A
Author version available: Download Author version (PDF)
Citation: Mol. BioSyst., 2015,11, 2900-2906
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    Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

    R. Kanji, A. Sharma and G. Bagler, Mol. BioSyst., 2015, 11, 2900
    DOI: 10.1039/C5MB00312A

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