Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

中文翻译：

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在药物发现中使用深度学习预测药物不良事件

用于检测药物副作用的传统机器学习方法带来了重大挑战，因为特征工程过程是劳动密集型的、依赖专家的、耗时且成本效益低的。此外，这些方法仅侧重于检测药物与其副作用之间的关联或对药物-药物相互作用进行分类。在技​​术进步和大数据可用性的推动下，我们提供了使用深度学习方法检测和分类副作用的综述。研究表明，异构、多维药物数据源的有效整合，以及深度学习方法的创新部署，有助于减少或预防药物不良反应（ADR）的发生。