In this study, we developed a hybrid deep learning (DL) model, which is one of the first interpretable hybrid DL models with Inception modules, to give a descriptive prediction of drug side‐effects. The model consists of a graph convolutional neural network (GCNN) with Inception modules to allow more efficient learning of drug molecular features and bidirectional long short‐term memory (BiLSTM) recurrent neural networks to associate drug structure with its associated side effects. The outputs from the two networks (GCNN and BiLSTM) are then concatenated and a fully connected network is used to predict the side effects of drugs. Our model achieves an AUC score of 0.846 irrespective of what classification threshold is chosen. It has a precision score of 0.925 and the Bilingual Evaluation Understudy (BLEU) scores obtained were 0.973, 0.938, 0.927, and 0.318 which show significant achievements despite the fact that a small drug data set is used for adverse drug reaction (ADR) prediction. Moreover, the model is capable of accurately structuring correct words to describe drug side‐effects and associates them with its drug name and molecular structure. The predicted drug structure and ADR relation will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during early phases of drug development. It can also help detect unknown ADRs embedded in existing drugs, hence contributing significantly to the science of pharmacovigilance.

中文翻译：

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使用混合深度学习模型的药物副作用的描述性预测

在这项研究中，我们开发了一种混合深度学习（DL）模型，该模型是第一个具有Inception模块的可解释的混合DL模型之一，可以对药物的副作用进行描述性预测。该模型由带卷积神经网络（GCNN）的Inception模块组成，可以更有效地学习药物分子特征，并使用双向长短期记忆（BiLSTM）递归神经网络将药物结构与其相关的副作用联系起来。然后将两个网络（GCNN和BiLSTM）的输出连接起来，并使用一个完全连接的网络来预测药物的副作用。无论选择哪种分类阈值，我们的模型均获得0.846的AUC评分。它的精确度得分为0.925，双语评估研究（BLEU）得分为0.973、0.938、0.927，和0.318取得了显著成就，尽管事实是使用小的药物数据集进行了药物不良反应（ADR）预测。此外，该模型能够准确构造正确的词来描述药物副作用，并将其与药物名称和分子结构相关联。预测的药物结构和ADR关系将为临床前安全药理研究提供参考，并有助于在药物开发的早期阶段识别ADR。它还可以帮助检测嵌入在现有药物中的未知ADR，从而对药物警戒科学做出重大贡献。该模型能够准确地构造正确的词来描述药物的副作用，并将其与药物名称和分子结构相关联。预测的药物结构和ADR关系将为临床前安全药理研究提供参考，并有助于在药物开发的早期阶段识别ADR。它还可以帮助检测嵌入在现有药物中的未知ADR，从而对药物警戒科学做出重大贡献。该模型能够准确地构造正确的词来描述药物的副作用，并将其与药物名称和分子结构相关联。预测的药物结构和ADR关系将为临床前安全药理研究提供参考，并有助于在药物开发的早期阶段识别ADR。它还可以帮助检测嵌入在现有药物中的未知ADR，从而对药物警戒科学做出重大贡献。