Abstract
Introduction: Adverse drug reactions (ADRs) are responsible for the failure of a significant portion of investigative drugs trials and the major reason for the withdrawal of drugs from clinical research. A number of ADRs are caused by the (undesired) interaction of drugs with key proteins involved in normal biological processes. Identification of these ADR-related proteins facilitates the design of drugs with fewer adverse effects by rationally avoiding unwanted interaction with these proteins.
Method: This work explores the use of a statistical learning method, support vector machines (SVMs), for the identification of potential ADR-related proteins. A SVM classification system was trained and tested by using 759 ADR-related proteins of different species and 2280 non-ADR-related proteins.
Results:93.9% of the ADR-related proteins and 98.2% of non-ADR-related proteins were correctly classified.
Discussion: The SVM is potentially useful for facilitating the identification of ADR-related proteins. The development of methods to identify ADR indications of ADR-related proteins are progressing well, an example of which is the web-based ADR-related protein prediction tool SVMDART, which can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/dart.cgi.
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This work is supported by National Natural Science Foundation of China (NSFC grant no. 30400573). The authors do not have any potential conflicts of interest in relation to this article.
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Ji, Z.L., Han, L.Y., Zheng, C.J. et al. Prediction of Putative Adverse Drug Reaction-Related Proteins from Primary Sequence by Support Vector Machines. Int J Pharm Med 19, 317–322 (2005). https://doi.org/10.2165/00124363-200519050-00009
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DOI: https://doi.org/10.2165/00124363-200519050-00009