Quantitative prediction of drug side effects based on drug-related features

Original Research Article
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Abstract

Motivation

Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them.

Methods

In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model.

Results

Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

Keywords

Drugs Side effects Quantitative prediction Features Ensemble learning 
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Notes

Acknowledgement

This work is supported by the National Science Foundation of China (11226267, 61103126,61572368) and the Fundamental Research Funds for the Central Universities (2042017kf0219).

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Copyright information

© Springer-Verlag 2017

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsSouth-central University for NationalitiesWuhanChina
  2. 2.School of ComputerWuhan UniversityWuhanChina

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