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Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities

Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 1903)

Abstract

In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be evaluated based on the output of an SVM, which was binary, the proposed method evaluated the reliability as a product of a distance from the separating hyperplane of the SVM and a similarity between the disease targeted by the drug and a candidate disease. A validation using real data revealed that the performance of the proposed method was promising.

Key words

Drug repositioning Machine learning Support vector machine (SVM) Side effect Chemical structure Drug target Reliability score 
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Notes

Acknowledgments

The author thanks Mr. Kohei Adachi for his contribution toward an early version of this work.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Kogakuin UniversityTokyoJapan

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