Motivation Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic profiles. However, existing methods ignore the potential relationships among genes. In addition, similarity among cell lines/drugs was rarely considered explicitly. Results We propose a novel DRP framework, called TGSA, to make better use of prior domain knowledge. TGSA consists of Twin Graph neural networks for Drug Response Prediction (TGDRP) and a Similarity Augmentation (SA) module to fuse fine-grained and coarse-grained information. Specifically, TGDRP abstracts cell lines as graphs based on STRING protein–protein association networks and uses Graph Neural Networks (GNNs) for representation learning. SA views DRP as an edge regression problem on a heterogeneous graph and utilizes GNNs to smooth the representations of similar cell lines/drugs. Besides, we introduce an auxiliary pre-training strategy to remedy the identified limitations of scarce data and poor out-of-distribution generalization. Extensive experiments on the GDSC2 dataset demonstrate that our TGSA consistently outperforms all the state-of-the-art baselines under various experimental settings. We further evaluate the effectiveness and contributions of each component of TGSA via ablation experiments. The promising performance of TGSA shows enormous potential for clinical applications in precision medicine. Availability and implementation The source code is available at https://github.com/violet-sto/TGSA. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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TGSA：基于蛋白质-蛋白质关联的双图神经网络，用于具有相似性增强的药物反应预测

动机 药物反应预测 (DRP) 在精准医学（例如癌症分析和治疗）中起着重要作用。深度学习算法的最新进展使根据遗传图谱准确预测药物反应成为可能。然而，现有方法忽略了基因之间的潜在关系。此外，很少明确考虑细胞系/药物之间的相似性。结果 我们提出了一个新的 DRP 框架，称为 TGSA，以更好地利用先验领域知识。TGSA 由用于药物反应预测 (TGDRP) 的双图神经网络和用于融合细粒度和粗粒度信息的相似性增强 (SA) 模块组成。具体来说，TGDRP 基于 STRING 蛋白质-蛋白质关联网络将细胞系抽象为图，并使用图神经网络 (GNN) 进行表征学习。SA 将 DRP 视为异构图上的边回归问题，并利用 GNN 来平滑相似细胞系/药物的表示。此外，我们引入了一种辅助预训练策略来弥补数据稀缺和分布外泛化能力差的局限性。在 GDSC2 数据集上进行的大量实验表明，我们的 TGSA 在各种实验设置下始终优于所有最先进的基线。我们通过消融实验进一步评估 TGSA 每个组件的有效性和贡献。TGSA 的良好性能显示了其在精准医学临床应用中的巨大潜力。可用性和实施​​源代码可在 https://github.com/violet-sto/TGSA 获得。