arXiv:2606.01394v1 Announce Type: new Abstract: Systematic characterization of drug-disease relationships is essential for drug discovery and repurposing, yet is hindered by the heterogeneity and rapid growth of biomedical literature. Existing datasets rely on labor-intensive curation and are often incomplete, while LLM-only approaches suffer from hallucination and weak evidence grounding. We introduce UniD$^3$, a unified framework that integrates Large Language Models with Knowledge Graph-enhanced Retrieval-Augmented Generation (KG-RAG) to extract, organize, and validate drug-disease knowledg
The rapid advancement of Large Language Models (LLMs) and the increasing need for efficient drug discovery methods are converging, pushing for intelligent systems that can navigate vast and complex biomedical data.
This development represents a significant step towards automating and accelerating drug discovery and repurposing, potentially leading to faster development of new treatments and more efficient allocation of research resources.
The integration of LLMs with Knowledge Graphs via RAG for drug-disease discovery moves beyond labor-intensive manual curation and addresses LLM hallucination, offering a more reliable and scalable approach to biomedical knowledge extraction and validation.
- · Pharmaceutical companies
- · Biotech startups
- · AI/ML researchers in biomedical field
- · Patients with complex diseases
- · Traditional drug discovery methods
- · Manual data curation services
Accelerated identification of drug candidates and disease mechanisms.
Reduced R&D costs and shortened timelines for drug development, increasing the pace of medical innovation.
Potential for personalized medicine to become more prevalent and effective due to deeper insights into drug-disease relationships at scale.
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