Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its unexpected adverse side effects and must be identified at an early stage of drug discovery and development. Many computational methods have been proposed for this purpose, but most require specific types of information, or they have less concern in interpretation on underlying genes. We propose a deep learning-based framework for DDI prediction with drug-induced gene expression signatures so that the model can provide the expression level of interpretability for DDIs. The model engineers dynamic drug features using a gating mechanism that mimics the co-administration effects by imposing attention to genes. Also, each side-effect is projected into a latent space through translating embedding. As a result, the model achieved an AUC of 0.889 and an AUPR of 0.915 in unseen interaction prediction, which is competitively very accurate and outperforms other state-of-the-art methods. Furthermore, it can predict potential DDIs with new compounds not used in training. In conclusion, using drug-induced gene expression signatures followed by gating and translating embedding can increase DDI prediction accuracy while providing model interpretability. The source code is available on GitHub ( https://github.com/GIST-CSBL/DeSIDE-DDI ).

中文翻译：

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DeSIDE-DDI：使用药物诱导的基因表达对药物相互作用的可解释性预测

药物-药物相互作用 (DDI) 是多药治疗的一个主要问题，因为它具有意想不到的副作用，必须在药物发现和开发的早期阶段进行识别。为此目的已经提出了许多计算方法，但大多数都需要特定类型的信息，或者它们不太关心对潜在基因的解释。我们提出了一个基于深度学习的 DDI 预测框架，该框架具有药物诱导的基因表达特征，以便该模型可以为 DDI 提供可解释性的表达水平。该模型使用一种门控机制来设计动态药物特​​性，该机制通过对基因的关注来模仿共同给药的效果。此外，每个副作用都通过翻译嵌入投射到潜在空间中。结果，该模型实现了 0.889 的 AUC 和 0 的 AUPR。915 在看不见的交互预测中，具有竞争力，非常准确，并且优于其他最先进的方法。此外，它还可以预测训练中未使用的新化合物的潜在 DDI。总之，使用药物诱导的基因表达特征，然后进行门控和翻译嵌入可以提高 DDI 预测准确性，同时提供模型可解释性。源代码可在 GitHub (https://github.com/GIST-CSBL/DeSIDE-DDI) 上获得。