Cancer has long been a main cause of human death, and the discovery of new drugs and the customization of cancer therapy have puzzled people for a long time. In order to facilitate the discovery of new anti-cancer drugs and the customization of treatment strategy, we seek to predict the response of different anti-cancer drugs with variational autoencoders (VAE) and multi-layer perceptron (MLP).Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data, and encode these data with {\sc {GeneVae}} model, which is an ordinary VAE, and rectified junction tree variational autoencoder ({\sc JtVae}) (\cite{jin2018junction}) model, respectively. Encoded features are processes by a Multi-layer Perceptron (MLP) model to produce a final prediction. We reach an average coefficient of determination ($R^{2} = 0.83$) in predicting drug response on breast cancer cell lines and an average $R^{2} > 0.84$ on pan-cancer cell lines. Additionally, we show that our model can generate unseen effective drug compounds for specific cancer cell lines.

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用于抗癌药物反应预测的变分自编码器

癌症长期以来一直是人类死亡的主要原因，新药的发现和癌症治疗的定制化一直困扰着人们。为了促进新的抗癌药物的发现和治疗策略的定制，我们试图用变分自编码器（VAE）和多层感知器（MLP）来预测不同抗癌药物的反应。我们的模型取输入癌细胞系的基因表达数据和抗癌药物分子数据，并用{\sc {GeneVae}}模型对这些数据进行编码，这是一个普通的VAE，以及整流结树变分自编码器（{\sc JtVae}）（ \cite{jin2018junction}) 模型。编码特征是由多层感知器 (MLP) 模型处理以产生最终预测。我们达到平均决定系数 ($R^{2} = 0。83$) 在预测对乳腺癌细胞系的药物反应和平均 $R^{2} > 0.84$ 对泛癌细胞系。此外，我们表明我们的模型可以为特定的癌细胞系生成看不见的有效药物化合物。