Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
Comput Biol Med. 2024 Mar;170:108007. doi: 10.1016/j.compbiomed.2024.108007. Epub 2024 Jan 15.
Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.
药物组合常用于治疗癌症，以减少副作用并提高疗效。对于大型数据集，实验发现药物组合协同作用既耗时又昂贵。因此，需要一种高效可靠的计算方法来研究这些药物组合。深度学习的进步可以处理具有各种生物学问题的大型数据集。在这项研究中，我们开发了一种基于图转换器网络的协同 GTN 模型，用于预测针对未经处理的癌细胞系表达谱的协同药物组合。我们通过图表示药物，其中图的每个节点和边分别包含九种原子特征向量和四种键特征。细胞系基于其基因表达谱表示。将药物图通过 GTN 层传递，以提取每个药物对的广义特征图。提取的药物对特征和细胞系基因表达谱被串联，并随后通过多个密集连接层进行处理。SynergyGTN 优于最先进的方法，在 5 倍交叉验证中，接收器操作特征曲线下面积提高了 5%。通过三种交叉验证测试策略，即留药、留组合和留组织，进一步验证了 SynergyGTN 的准确性，分别提高了 8%、1%和 2%。Astrazeneca Dream 数据集被用作独立数据集来验证和评估所提出方法的泛化能力，导致平衡准确性提高了 13%。总之，SynergyGTN 是一种可靠且高效的计算方法，用于预测癌症治疗中的药物组合协同作用。最后，我们开发了一个网络服务器工具，以方便制药行业和研究人员使用，网址为：http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/。
