Baidu, Inc., 701, Na Xian Road, 201210, Shanghai, China.
Baidu, Inc., Xue Fu Road, 518000, Shenzhen, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac302.
Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical-gene-tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure-gene and gene-gene associations and a coarse-granularity transformer encoder to extract chemical-chemical and chemical-cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.
药物联合疗法在许多方面优于癌症治疗的单药疗法。由于可能的药物对巨大搜索空间的湿实验耗时过程，通过筛选来识别新的药物组合具有挑战性。因此，已经开发了计算方法来预测具有潜在协同作用的药物对。尽管当前模型取得了成功，但从化学-基因-组织相互作用的角度理解药物协同作用的机制缺乏研究，阻碍了当前算法对药物机制的研究。在这里，我们提出了一种基于多头注意力机制的深度神经网络模型 DTSyn（用于药物对协同预测的双 Transformer 编码器模型），用于识别新的药物组合。我们设计了一个细粒度的 Transformer 编码器来捕获化学亚结构-基因和基因-基因的关联，以及一个粗粒度的 Transformer 编码器来提取化学-化学和化学-细胞系的相互作用。DTSyn 在四个不同的交叉验证任务上实现了最高的接收器操作特征曲线下面积为 0.73、0.78、0.82 和 0.81，优于所有竞争方法。此外，DTSyn 在五个独立数据集上实现了最佳的真阳性率 (TPR)。消融研究表明，两个 Transformer 编码器块都有助于 DTSyn 的性能。此外，DTSyn 可以提取化学物质和细胞系之间的相互作用，代表药物作用的潜在机制。通过利用注意力机制和预先训练的基因嵌入，DTSyn 显示出了提高的可解释性能力。因此，我们设想我们的模型是一种有价值的工具，可以优先考虑具有化学和细胞系基因表达谱的协同药物对。
