## 5 Discussion
One such study introduced GATBoost [178], a comprehensive framework designed to improve property prediction in polymers. This research demonstrated the power of GATs in mining important substructures within polymer graphs that are highly correlated with key properties such as the glass transition temperature (Tg). By integrating GATs with XGBoost-based supervised learning, the study achieved high accuracy in predictions, significantly enhancing the efficiency of property prediction processes and reducing experimental time. This approach not only delivers precise predictions but also offers direct visualization of the crucial polymer substructures, underscoring the interpretability of GAT models in material science. In the realm of drug discovery, the AttentionMGT-DTA [179] model presents a multimodal attention-based approach for predicting drug-target affinity (DTA), a critical step in the drug design process. By utilizing GATs to represent molecular graphs and binding pocket graphs, and integrating two attention mechanisms to explore interactions between different protein modalities and drug-target pairs, this model has set new benchmarks in DTA prediction accuracy. The high interpretability of AttentionMGT-DTA, particularly in modeling interaction strengths between drug atoms and protein residues, further highlights the capability of GATs to enhance understanding and decision-making in drug development. Another significant contribution this year comes from the bioinformatics domain with the ML-FGAT model [180], which addresses the prediction of multi-label protein subcellular localization (SCL). This model combines GATs with feature-generative adversarial networks and linear discriminant analysis to predict protein SCL with remarkable accuracy. The study emphasizes the robustness and interpretability of GATs, particularly through the analysis of attention weight parameters, and demonstrates their effectiveness across diverse datasets, including newly constructed ones such as human, virus, plant, and SARS-CoV-2. These studies collectively underscore the rapidly growing interest in and application of GATs in cutting-edge research, reflecting their versatility and potential to revolutionize complex problem-solving across a wide range of scientific disciplines. Despite the significant advancements and diverse applications of graph attention networks (GATs), several limitations and challenges remain that hinder their broader applicability and effectiveness in certain scenarios. One of the primary concerns lies in the scalability of GATs when applied to large-scale graphs. As the size of the graph increases, the computational and memory demands of attention mechanisms, especially those that require full pairwise attention computations, can become prohibitive. This issue is exacerbated when dealing with dense graphs, where the number of edges grows quadratically with the number of nodes, leading to an explosion in the number of attention coefficients that need to be computed and stored. Moreover, the attention mechanism's reliance on softmax normalization can introduce challenges related to numerical stability and gradient vanishing, particularly in deeper networks with multiple attention layers. These factors can limit the practical usability of GATs in large-scale, real-world applications, where computational efficiency and scalability are crucial. Another significant limitation of GATs is their susceptibility to overfitting, especially when dealing with small or noisy datasets. The flexibility and expressiveness of attention mechanisms, while beneficial in capturing intricate patterns and relationships within the data, can also lead to the model learning spurious correlations that do not generalize well to unseen data. This issue is particularly pronounced in Variational GATs, where the introduction of probabilistic regularization techniques, although aimed at mitigating overfitting, may not always be sufficient in complex, multimodal environments.