Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human–AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.
协同药物组合在提升疗效与降低不良反应方面展现出巨大潜力。然而，由于疾病信号通路的因果机制尚未明确，有效且具有协同作用的药物组合预测仍是一个开放性问题。尽管已有多种深度学习（AI）模型被提出用于定量预测药物组合的协同效应，但现有深度学习方法的主要局限在于其本质缺乏可解释性，这使得AI模型的结论对医学专家而言缺乏透明度，从而限制了模型结论的稳健性及其在现实世界人机协同医疗中的应用能力。本文开发了一种可解释的图神经网络（GNN），通过挖掘关键子分子网络，揭示潜在的重要治疗靶点及协同作用机制（MoS）。该可解释GNN预测模型的核心是一种新型图池化层——基于自注意力机制的节点与边池化层（简称SANEpool），能够根据基因组特征与拓扑结构计算基因及其连接的重要性注意力分数。因此，所提出的GNN模型基于检测到的关键子分子网络，为预测和解释药物组合协同效应提供了系统化方法。在多个广泛采用的药物协同预测数据集上的实验表明：（1）SANEpool模型具有卓越的预测能力，可生成准确的协同效应评分预测；（2）SANEpool检测到的子分子网络具备自解释性，并对识别协同药物组合具有显著意义。
肿瘤(癌症)患者之家