# Drug-Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph
## 4 Discussion
### Strengths and Limitations  
This study proposes an efficient cross-modal feature fusion model, CM-DTA, for drug-target affinity prediction. CM-DTA is based on the multi-perceptive neighborhood self-attention aggregation strategy, which captures both first-order and second-order neighborhood information to enhance the structural perception of the graph modality. It also incorporates the cross-modal bidirectional adaptive guided fusion strategy, establishing effective interactions between the text and graph modalities, enabling features to focus on each other's key information for efficient cross-modal information fusion. Furthermore, the model leverages the explicit prediction strategy based on multi-head collaborative attention, which deeply explores the complex relationships between drug and target features, improving predictive performance and biological interpretability. Experimental results on the Davis and KIBA datasets demonstrate that CM-DTA significantly outperforms the current SOTA models. However, the model's performance may be influenced by the quality and size of the training data, as it relies heavily on large, well-labeled datasets for accurate predictions. Additionally, while the model effectively captures cross-modal interactions, it may still struggle with extremely noisy or incomplete data, potentially affecting its robustness.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Traditional Experimental**} & \multicolumn{2}{c}{**Computational**} & \multicolumn{2}{c}{**Deep Learning-Based**} \\ \cline{2-5}  & **SPR, ITC, ELISA, etc.** & **Ligand-Based or Structure-Based** & **Single-Modal-Based** & **Cross-Modal-Based** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Data \\ Requirements \\ \end{tabular} } & \multirow{3}{*}{None} & Long-term reliance on manually labeled and extracted data by experts & Initial training requires labeled data, after which features can be automatically extracted \\ \hline Application & Only a few & \multirow{3}{*}{Small-scale datasets} & Large-scale datasets & Very large-scale datasets \\ \cline{1-1}  & & drugs or proteins & & datasets \\ \cline{1-1} \cline{2-5} Computational & \multirow{2}{*}{Up to ten years} & \multirow{2}{*}{Three to five years} & \multirow{2}{*}{A few months to five years} \\ \cline{1-1}  & & & & \\ \cline{1-1} \cline{2-5} Interpretability & \begin{tabular}{c} High, \\ experimental results \\ are easy to understand \\ \end{tabular} & \begin{tabular}{c} Moderate, \\ limited by feature \\ extraction methods \\ \end{tabular} & \begin{tabular}{c} High, \\ limited by feature \\ extraction methods \\ \end{tabular} \\ \hline Significant & \begin{tabular}{c} High experimental \\ accuracy and \\ interpretability \\ \end{tabular} & \begin{tabular}{c} Efficient \\ computation \\ \end{tabular} & \begin{tabular}{c} Can automatically \\ extract features \\ \end{tabular} & \begin{tabular}{c} Comprehensive analysis \\ of multiple modalities, \\ able to leverage complex \\ structural relationships \\ and interactions \\ \end{tabular} \\ \hline Significant & \begin{tabular}{c} High cost, \\ time-consuming, \\ and small-scale \\ \end{tabular} & \begin{tabular}{c} Long-term reliance \\ on manual labeling, \\ small-scale \\ \end{tabular} & \
\begin{tabular}{c} Single modal information \\ leads to low utilization \\ \end{tabular} &
\begin{tabular}{c} Relics on \\ high-performance \\ hardware devices \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of existing research methods and cross-modal fusion of text and graph methods.