药物协同作用是药物再利用的重要组成部分，它解决了药物开发缓慢和多种疾病缺乏相应药物的问题。预测药物协同关系可以提前筛选药物组合，减少实验室资源浪费。在这项研究中，我们提出了一种利用图形自动编码器和卷积神经网络来预测药物协同作用 (GAECDS) 的模型。我们的方法包括图卷积神经网络作为编码器来编码药物特征，并使用矩阵分解方法作为解码器。多层感知器 (MLP) 用于处理细胞系特征并将其与药物特征相结合。此外，在编码过程中生成的潜在向量被用于使用卷积神经网络预测药物协同分数。通过使用 AUC、AUPR 和 F1 分数测量预测性能，GAECDS 优于其他最先进的模型。此外，发现预测的前 10 种药物组合中有四对效果很好，可以进行评估。案例研究表明，GAECDS 方法可用于识别潜在的药物协同作用。
图形概要
Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy.
Graphical Abstract
