Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.
药物组合的协同作用在提高药物疗效或者减轻药物毒副作用方面非常重要。然而由于药物之间复杂的作用机制，通过试验筛选新的药物组合需要巨大的成本。众所周知，运用计算模型的虚拟筛选，可以有效降低试验成本。最近，国外学者运用深度学习模型 DeepSynergy，成功预测了新的药物组合在癌症细胞系上的协同作用值。在本研究中，针对 DeepSynergy 采用 two-stage 方法和输入特征单一的问题，我们提出了一种新的端到端的深度学习模型 MulinputSynergy。该模型通过整合癌症细胞的基因表达、基因突变、基因拷贝数特征和抗癌药物化学特征来预测药物组合的协同作用值。为了解决细胞系某些特征维度数高的问题，我们使用卷积神经网络对基因特征降维。实验结果表明，本文提出的模型优于 DeepSynergy 深度学习模型，均方误差从 197 下降到 176，平均绝对误差从 9.48 下降到 8.77，决定系数从 0.53 提升到 0.58。研究结果表明本文模型能从多种特征中学习抗癌药物与细胞系的潜在关系，并且快速准确地定位有效的药物组合。.
Keywords: anticancer drugs; deep learning; drug combination; gene expression.