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

Interdisciplinary Sciences, Computational Life Sciences
Yanqing Niu, Wen Zhang

Abstract

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. 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. Since weights for side effects are empirical values, we randomly generate different weights i...Continue Reading

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Citations

Feb 7, 2018·International Journal of Molecular Sciences·Li-Yue BaiDong-Qing Wei
May 27, 2020·Computational and Mathematical Methods in Medicine·Haiyan LiangXiaolin Zhang

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