A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.

中文翻译：

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使用深度学习模型有效预测药物-药物相互作用。

药物-药物相互作用或药物协同作用被广泛用于癌症治疗。然而，药物-药物相互作用的预测被定义为一个不适定问题，因为手动测试仅适用于一小部分药物。最近，预测药物-药物相互作用评分一直是一个热门的研究课题。最近，文献中提出了许多机器学习模型来有效地预测药物-药物相互作用评分。然而，这些模型存在过拟合问题。因此，这些模型对于预测药物-药物相互作用评分并不是那么有效。在这项工作中，提出并实现了一种集成的卷积混合密度递归神经网络。所提出的模型集成了卷积神经网络、循环神经网络和混合密度网络。