## 7 Conclusions  
The present study describes a polarity-aware classification framework based on LoRA-tuned BiomedBERT that can tell the difference between synergistic and antagonistic DDIs using sentence-level input. The system was trained on a dataset with polarity labels that came from DrugComb and DrugBank, and it was tested on a set that did not have any drug entities that were also in the training set.  
This method looks promising for use in clinical support tools, especially in cases where structured labels are not available but there is still access to sentence-level interaction descriptions. It is efficient enough to run in limited environments and does not require full model retraining. We have not tested the model yet on clinical data like patient records or hospital notes. Right now, it only works on labeled sentences from public datasets. Before it can be used in practice, it needs to be tested in more realistic settings.  
One of the next things we will do is expand the labels. For now, it only handles synergism and antagonism. We also plan to run it on other biomedical text sources to see how well it holds up outside this setup.  
Conceptualization, I-F.G. and V.-I.B.; methodology, I-F.G. and V.-I.B.; software, I-F.G.; validation, I-F.G., V.-I.B., and L.B.I.; formal analysis, I-F.G., V.-I.B., L.B.I.; investigation, I-F.G.; writing--original draft preparation, I-F.G.; writing--review and editing, I-F.G., V.-I.B., and L.B.I.; supervision, L.B.I.; funding, I-F.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Study** & **Model** & **Dataset** &
\begin{tabular}{l} **New Drug** \\ **Accuracy** \\ \end{tabular} & **Notes** \\ \hline [PERSON] et al. [12] & SynerGNet & Cell-line synergy data & 84.1\% & Requires omics data \\ [PERSON] et al. [18] & HAN-DDI & Heterogeneous biomedical graph & 82.87\% & Limited sentence-level \\
**Our fine-tuned LoRA-** & LoRA-BiomedBERT & DrugBank v5.1 + Drug- & **85.42\%** & Lightweight, no omics needed \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative performance of recent DDI classification models.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** The training dataset is derived from DrugBank and DrugComb resources that are not redistributable under an open license. As such, the raw data cannot be shared publicly. Inference logs and evaluation metrics can be made available upon request. The fine-tuning code is not released at this time due to pending extensions for follow-up studies.  
**Acknowledgments:** The authors gratefully acknowledge the support of the Research Center on Artificial Intelligence, Data Science, and Smart Engineering (ARTEMIS) at the George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Romania. Additional support was provided by the CA22137 COST Action and the Randomized Optimization Algorithms Research Network (ROAR-NET). The authors also thank the Research Group on Artificial Intelligence and Data Science for Healthcare Innovation (REFLECTION) for its insightful contributions.  
The authors declare no conflicts of interest.