Abstract
The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.
Highlights
The safety assessment of candidate chemical compounds is essential for drug development
It’s reasonable to presume that integrating chemical and biological information will help boost the accuracy of side effect prediction
It is showed that drugs with more common side effects display significantly stronger similarity in all types of features. These results imply that similar drugs possess similar side effect labels. To further verify this assumption, we calculated the inner products between the columns of the feature matrix Xp and the columns of the side effect matrix Y and utilized these products to represent the relationships between drug features and side effect labels
Summary
The safety assessment of candidate chemical compounds is essential for drug development. Detection of serious adverse effects of drugs in preclinical tests or clinical trials is one of the major reasons for the failure of drug development [1]. Liang et al J Cheminform (2019) 11:79 gene expression changes were summarized as biological process terms to predict side effects [13]. It’s reasonable to presume that integrating chemical and biological information will help boost the accuracy of side effect prediction. Yamanishi et al predicted side effects by integrating chemical structures and target protein data of drugs [14]. Wang et al prioritized drug side effects by combining chemical structures and gene expression changes [15]. There are some studies that include other information such as phenotypic data of drugs to predict side effects [16, 17]
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