Predicting the outcome of antiretroviral therapies for HIV-1 is a pressing clinical challenge, especially when the treatment regimen includes drugs for which limited effectiveness data is available. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN). The FC network employs tabular data with a feature vector made up of viral mutations identified in the most recent genotypic resistance test, along with the drugs used in therapy. Conversely, the GNN leverages knowledge derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence, to build informative graphs. We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model, especially when considering Out-of-Distribution drugs. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in real-world applications with limited data availability. This research highlights the potential of our approach to inform antiretroviral therapy outcome prediction and contribute to more informed clinical decisions.

翻译：预测HIV-1抗逆转录病毒治疗的结局是一个紧迫的临床挑战，尤其是在治疗方案中包含有效性数据有限的药物时。这种数据稀缺可能源于新药上市或临床使用范围有限。为解决这一问题，我们提出了一种新型联合融合模型，该模型整合了全连接神经网络与图神经网络的特征。全连接网络利用表格数据，以包含最新基因型耐药测试中识别的病毒突变特征向量及治疗药物构成的特征向量作为输入。相反，图神经网络利用从斯坦福HIV耐药突变表中获取的知识构建信息丰富的图结构，这些表格作为基于病毒基因序列推断体内治疗效果的基准参考。我们评估了这些模型在测试集中对分布外药物的鲁棒性，特别关注图神经网络在此类场景中的作用。综合分析表明，所提模型在全连接模型基础上表现出持续的性能提升，尤其是在处理分布外药物时。这些结果凸显了将斯坦福评分整合到模型中的优势，不仅增强了模型的泛化能力和鲁棒性，还扩展了其在数据有限的真实场景中的实用性。本研究揭示了该方法在抗逆转录病毒治疗结局预测中的潜力，有助于支持更明智的临床决策。