The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction. We summarize and examine state-of-the-art deep learning methods that have been published recently. Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear in the training dataset. In particular, all the five evaluated deep learning methods performed worst than the similarity-regularized matrix factorization (SRMF) method in our drug blind test. We outline the challenges in applying deep learning approach to drug response prediction and suggest unique opportunities for deep learning integrated with established bioinformatics analyses to overcome some of these challenges.

中文翻译：

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深度学习可以在多大程度上改善对癌细胞系药物反应的预测？

药物反应预测问题源于个性化医疗和药物发现。深度神经网络已应用于可用于 1000 多种癌细胞系和组织的多组学数据，以更好地预测药物反应。我们总结并检查了最近发表的最先进的深度学习方法。尽管深度学习方法在药物反应预测方面取得了重大进展，但深度学习方法在预测训练数据集中未出现的药物反应方面表现出弱点。特别是，在我们的药物盲测中，所有五种评估的深度学习方法都比相似正则化矩阵分解（SRMF）方法表现最差。