School of Computer Science and Engineering, Central South University, China.
School of David R. Cheriton School of Computer Science, University of Waterloo, Canada.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab449.
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.
药物的副作用在医疗保健系统中引起了越来越多的关注。准确识别药物的副作用对于药物开发和风险评估非常重要。已经开发了一些计算模型来预测药物的潜在副作用，并取得了令人满意的性能。然而，大多数现有的方法只能预测副作用是否会发生，而不能确定副作用的频率。尽管有少数现有的方法可以预测药物副作用的频率，但它们强烈依赖于已知的药物-副作用关系。因此，它们不能应用于没有已知副作用频率信息的新药。在本文中，我们开发了一种新颖的基于相似性的深度学习方法，称为 SDPred，用于确定药物副作用的频率。与现有的最先进模型相比，SDPred 集成了丰富的特征，可用于预测新药物的副作用频率，而无需任何已知的药物-副作用关联或频率信息。据我们所知，这是第一个可以预测人群中新药物副作用频率的工作。比较结果表明，SDPred 明显优于所有以前报道的模型。此外，一些案例研究也证明了我们提出的方法在实际应用中的有效性。SDPred 软件和数据可在以下网址免费获取：https://github.com/zhc940702/SDPred、https://zenodo.org/record/5112573 和 https://hub.docker.com/r/zhc940702/sdpred。
