Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our {\sc {GeneVae}} model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder ({\sc JTVae}) model, respectively. A multi-layer perceptron processes these encoded features to produce a final prediction. Our tests show our system attains a high average coefficient of determination ($R^{2} = 0.83$) in predicting drug responses for breast cancer cell lines and an average $R^{2} = 0.845$ for pan-cancer cell lines. Additionally, we show that our model can generates effective drug compounds not previously used for specific cancer cell lines.
翻译:癌症是造成人类死亡的主要原因,但发现药物和定制癌症治疗是昂贵和费时的。我们寻求促进发现新的药物和癌症治疗战略,使用变异自动编码器(VAEs)和多层透视器(MLPs)来预测抗癌药物反应。我们的模型将癌症细胞线和抗癌症药物分子数据作为基因表达数据输入,并将这些数据与我们(sc {GeneVae}模型(普通VAE模型)和纠正的接合树体自动变形器(Hsc JTVae})模型编码。我们测试显示,我们的系统在预测乳腺癌细胞线的药物反应方面达到一个高平均确定系数(R%2}=0.83美元),而普通的 $R ⁇ 2} =0.845美元。此外,我们显示,我们的模型可以产生有效药物化合物,而以前没有用于特定癌症细胞线。