## 2 Literature Review  
Recent rule-centric engines use metabolic ontologies and curated CYP-450 tables to detect pharmacokinetic clashes but still miss pharmacodynamic synergies. For example, in a seminal study [9], drug database providers were found to update their interaction rule bases monthly, with each update vetted by clinical experts to maintain more than 90% precision for recently approved drugs. [PERSON] and [PERSON] [10] showed that a Tanimoto similarity threshold above 0.85 can recover over 60% of true DDIs, although recall remains limited. Comprehensive surveys covering 2020-2024 report that graph neural networks and variational auto-encoders obtain AUROC between 0.82 and 0.86 on DrugBank pairs. [PERSON] et al. [11] employed a heterogeneous graph attention network(GAT) with chemical, gene-expression, and pathway edges. [PERSON] et al. [12] proposed the synergistic graph neural network (SynerGNet), a graph attention network tailored for predicting synergistic drug pairs in oncology. By integrating cell-line viability profiles and chemical descriptors, their model achieved a balanced accuracy of 84.1% on DrugComb-derived synergy data. However, it relies heavily on experimental omics inputs (e.g., gene expression and dose-response curves), limiting its direct applicability in real-world clinical decision support, where such data are unavailable at prescription time. Despite strong performance, the infrastructure demands and feature requirements of SynerGNet restrict its use to specialized research environments or pharmacological studies where rich experimental annotation is available. Other studies have used domain-adapted language models such as BioBERT, PubMedBERT, and BiomedBERT, which have outperformed their general-domain counterparts by 6-10 percentage points in the F1 score (the harmonic mean of precision and recall) on relation-extraction benchmarks [13]. [PERSON] et al. [14] demonstrated that incorporating sentence-level attention explanations improved pharmacist trust ratings in a simulated medication-review task. While domain-specific models like BioBERT, PubMedBERT, and BiomedBERT significantly improve biomedical language comprehension, they are not trained to recognize interaction polarity. These models learn general contextual embeddings but do not distinguish between synergistic and antagonistic effects unless explicitly fine-tuned on labeled polarity data. In this study, we use LoRA-based tuning to adapt BiomedBERT for this specific classification task. [PERSON] et al. [8] benchmarked LoRA against adapters and prefix-tuning on eight biomedical tasks, in which it decomposes weight updates into low-rank matrices--often rank 8-16--so that only 0.5-2% of parameters are trainable. LoRA matched full fine-tuning within 0.3 F1 points while reducing video random-access memory (VRAM) usage by 12\(\times\). In a different approach, [PERSON] et al. [15] applied confidence-weighted pseudo-labels to MEDI-SPAN entries--a widely used commercial drug database containing structured information about drug interactions, dosages, and contraindications--observing a 5-7% macro-F1 gain with only 1000 human annotations. Unlike their method, which applied fixed confidence thresholds during training, our framework logs all predictions along with their confidence scores. This allows for flexible, rule-based filtering or manual review after inference, without discarding potentially informative cases prematurely. Several earlier studies have improved predictive accuracy in DDI modeling by using molecular graph structures. One such method, for example, presented a graph neural network learning size-adaptive molecular substructures to capture chemically relevant interactions at several levels, enhancing pharmacological effect classification among compounds [16]. This graph-based framework highlights the predictive utility of structural drug features, though it does not explicitly address sentence-level interpretability or clinical deployment.