  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.
