肿瘤异质性对预测药物反应提出了重大挑战，特别是同一基因内的错义突变可能导致不同的结果，例如耐药性、敏感性增强或治疗无效。这些复杂的关系凸显了肿瘤学中对先进分析方法的需求。由于图卷积网络（GCN）处理异构数据的强大能力，它代表了一种有前途的预测药物反应的方法。然而，简单的二分图无法准确捕捉错义突变和药物反应中涉及的复杂关系。此外，药物反应的深度学习模型通常被认为是“黑匣子”，其可解释性仍然是一个广泛讨论的问题。为了应对这些挑战，我们提出了一种可解释的动态有向图卷积网络（IDDGCN）框架，该框架包含四个关键特征：（1）使用有向图来区分灵敏度和电阻关系，（2）节点权重的动态更新基于节点特异性相互作用，（3）探索同一基因内不同突变与药物反应之间的关联，以及（4）通过整合解释生物学意义的加权机制来增强可解释性模型，同时评估预测透明度的地面实况构建方法。实验结果表明，IDDGCN 优于现有的最先进模型，表现出出色的预测能力。对其可解释性的定性和定量评估进一步凸显了其解释预测的能力，为精准肿瘤学和靶向药物开发提供了新的视角。

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses. However, simple bipartite graphs cannot accurately capture the complex relationships involved in missense mutation and drug response. Furthermore, Deep learning models for drug response are often considered "black boxes", and their interpretability remains a widely discussed issue. To address these challenges, we propose an Interpretable Dynamic Directed Graph Convolutional Network (IDDGCN) framework, which incorporates four key features: (1) the use of directed graphs to differentiate between sensitivity and resistance relationships, (2) the dynamic updating of node weights based on node-specific interactions, (3) the exploration of associations between different mutations within the same gene and drug response, and (4) the enhancement of interpretability models through the integration of a weighted mechanism that accounts for the biological significance, alongside a ground truth construction method to evaluate prediction transparency. The experimental results demonstrate that IDDGCN outperforms existing state-of-the-art models, exhibiting excellent predictive power. Both qualitative and quantitative evaluations of its interpretability further highlight its ability to explain predictions, offering a fresh perspective for precision oncology and targeted drug development.