A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.