From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing (2025)

Abstract: Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer’s disease further confirming its robustness and effectiveness.

## introduction drug repurposing has emerged as an effective strategy to accelerate drug development by identifying new therapeutic uses for existing drugs @xcite @xcite . with the increasing complexity of biological systems and the growing availability of heterogeneous biomedical data @xcite , there is a critical need for computational approaches that can efficiently integrate and reason over these large-scale sources @xcite drug repurposing relies heavily on expert-driven analysis of medical literature and clinical data, requiring interdisciplinary collaboration across pharmacology, chemistry, and medicine @xcite . this process is time-consuming and resource-intensive, often leading to low throughput @xcite . recent advances in deep learning have improved drug repurposing performance by learning complex representations of drugs and targets from data @xcite @xcite , enhancing both accuracy and interpretability @xcite . however, these models often overlook structured domain knowledge from clinical research and biomedical ontologies @xcite . to address this, several works @xcite @xcite have incorporated knowledge graph embedding (kge) techniques to model biomedical entities and relations, enabling more interpretable and knowledgeaware drug repurposing. these methods leverage structured biomedical knowledge to improve prediction and support reasoning over complex biological interactions. however, an important yet underexplored aspect of biomedical kgs is the presence of generalpurpose biomedical concept knowledge-commonsense mechanistic constraints that are critical for safe and effective treatment decisions. for example, acetylcholinesterase inhibitors (e.g., donepezil) are effective for alzheimer's disease but contraindicated in parkinson's disease psychosis due to potential cholinergic overstimulation @xcite . such concept-level priors are rarely captured by structure-only kg embedding models, leading to limited reliability when generalizing to rare or mechanistically complex diseases. to address this gap, we propose lladr (large language model-assisted drug repurposing), a framework that integrates concept-level semantics extracted from llms into kg embeddings, promoting better representations of biomedical concepts (e.g., compound, disease, gene, and cell lines in figure 1 ). lladr generates enriched biomedical concept representations by prompting llms with textual discriptions and using these embeddings to guide kge fine-tuning. by injecting treatmentaware knowledge into kgs, lladr enhances both predictive accuracy and robustness in drug repurposing tasks. in summary, our main contributions are as follows: (1) we propose the first framework that integrates common-sense biomedical concept knowledge to enhance kg-based drug repurposing; ## lladr framework we present lladr (as shown in fig. 2 ), a novel framework specifically designed for fine-tuning knowledge graph embeddings through the strategic integration of textual information derived from open knowledge sources. this comprehensive framework consists of two core operational phases that work in tandem: (1) generating discription : during this initial stage, we employ advanced large language models (llms) such as gpt-4 to systematically generate rich, context-aware textual discriptions for every entity within the knowledge graph. these ai-generated discriptions are subsequently processed through state-of-the-art sentence embedding techniques like sentence-bert to create highdimensional vector representations, which are then stored alongside the original kg embeddings in parallel npy format files for entity-discription alignment. (2) knowledge graph fine-tuning: this crucial stage implements a multi-layered optimization approach where the initial drkg embeddings undergo enhancement through three synergistic mechanisms -firstly by concatenating textual embeddings through weighted averaging, secondly by applying geometric alignment constraints to preserve structural relationships, and thirdly by incorporating semantic consistency regularization terms that bridge the gap between symbolic kg representations and neural text embeddings. the iterative fine-tuning process leverages adaptive learning rate scheduling and contrastive loss functions to ultimately produce knowledge representations that simultaneously capture topological accuracy from the graph structure and nuanced semantic depth from open-domain textual knowledge. ## experimental setup datasets. we consider datasets that encompass various domains and sizes, ensuring comprehensive evaluation of the proposed model. specifically, we utilize the drkg dataset @xcite , a biomedical knowledge graph that includes drugs, diseases, genes, and cell lines, comprising 68,471 entities, 101 relationships, and 4,359,327 triples, which are partitioned into training (3,889,539 triples), validation (429,959 triples), and test (39,829 triples) sets to facilitate robust evaluation while preserving the integrity of the biomedical relationships. ## conclusion this study introduces lladr, a framework enhancing the drug repurposing knowledge graph (drkg) by combining structural embeddings with llm-derived semantics. it surpasses traditional methods in drug-disease prediction accuracy by jointly leveraging topological and contextual information. the approach captures structural and semantic relationships critical for drug discovery, applicable to biomedical kgs like disease-gene networks. future work may extend to larger graphs and multimodal data. in summary, lladr enhances drkg for drug repositioning, providing a scalable solution for knowledge graph improvements in drug discovery.