## 5 Experimental Evaluation
Figure 3: Dataset characteristics and input sentence analysis. (**a**) Label distribution in the training set, balanced between synergistic and antagonistic interactions. (**b**) Dataset size split across training (11,744), test (2936), and validation (192) sets, ensuring no drug overlap between them. (**c**) Distribution of input sentence lengths measured in characters; the mean length is 108, supporting efficient tokenization and uniform input formatting. In more concrete terms, it recognizes synergistic statements nine times out of ten (recall \(\approx\) 89.59%) and still identifies around 70.31% of antagonistic ones. These results demonstrate a solid performance for sentence-level DDI extraction, particularly given that only 11,744 parameters were updated during training (a fraction of the base model). Several follow-up experiments--varying LoRA rank, \(\alpha\)-scaling, dropout, and learning rate--exhibited the same pattern, that is, accuracy fluctuated within a narrow \(\pm\)2% band, while memory consumption and training time remained essentially constant. This stability suggests that the model's performance is largely driven by the pretrained biomedical priors of the backbone, rather than fine-tuned hyperparameter settings. Misclassifications tend to cluster around vague formulations such as \"Drug A may affect the activity of Drug B,\" where the sentence provides no explicit indication of benefit or harm. It is expected that recall for the _Antagonistic_ class will improve once such borderline examples are incorporated into the next pseudo-labeling round, thereby exposing the model to a broader variety of negative cues. We also checked how the model performs on completely new drug combinations--the ones it never saw during training. Table 4 shows a side-by-side comparison between the original BiomedBERT and the LoRA-tuned version. LoRA handled the unfamiliar data much better, with big improvements in F1 and precision. This suggests it does not just memorize, but can generalize to new cases. To better understand how the model performs on drugs it has not seen before, Figure 4 presents a side-by-side comparison of the baseline and LoRA-enhanced versions. In Figure 4a,b, we see that the original model fails to detect any synergistic interactions, whereas the LoRA variant manages to correctly identify both types with strong precision. The bar chart in Figure 4c breaks down the metric gains, showing the biggest improvements in F1 score and precision. ROC and AUC scores for each class are shown in Figure 4d,e, and they indicate that the model can separate synergistic and antagonistic cases with high reliability. Lastly, the curves in Figure 4f suggest that LoRA remains confident across a wide range of thresholds, which is useful when making real-world decisions based on prediction certainty. The results validate LoRA as a pragmatic compromise between performance and deployability. Achieving almost 86% accuracy on new drugs with only 11,744 trainable parameters (2936 test parameters and 192 parameters for validation) provides a credible foundation for integration into live CDSS modules. Meanwhile, the adapter's small footprint ensures ease of installation and version control. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Pretrained** & **LoRA** & **Improvement (\%)** \\ \hline Accuracy & 0.4896 & 0.8542 & +74.47\% \\ F1 & 0.3287 & 0.8539 & +159.81\% \\ Precision & 0.2474 & 0.8566 & +246.30\% \\ Recall & 0.4896 & 0.8542 & +74.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation set performance comparison.