# KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model

arXiv: https://arxiv.org/abs/2502.20350 · HTML: https://mlsanity.com/paper/2502.20350/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations](https://mlsanity.com/paper/2105.02711/) (2021)
- [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://mlsanity.com/paper/2409.04481/) (2024)
- [CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization](https://mlsanity.com/paper/2301.11223/) (2023)
- [Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review](https://mlsanity.com/paper/2309.06388/) (2023)

## Built on by (most cited first, 2 of 2)

- [Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing](https://mlsanity.com/paper/2506.03490/) (2025)
- [Few-Label Multimodal Modeling of SNP Variants and ECG Phenotypes Using Large Language Models for Cardiovascular Risk Stratification](https://mlsanity.com/paper/2510.16536/) (2025)

## Abstract (arXiv)

Drug discovery is a critical task in biomedical natural language processing (NLP), yet explainable drug discovery remains underexplored. Meanwhile, large language models (LLMs) have shown remarkable abilities in natural language understanding and generation. Leveraging LLMs for explainable drug discovery has the potential to improve downstream tasks and real-world applications. In this study, we utilize open-source drug knowledge graphs, clinical trial data, and PubMed publications to construct a comprehensive dataset for the explainable drug discovery task, named expRxRec. Furthermore, we introduce KEDRec-LM, an instruction-tuned LLM which distills knowledge from rich medical knowledge corpus for drug recommendation and rationale generation. To encourage further research in this area, we will publicly releasefootnoteA copy is attached with this submission both the dataset and KEDRec-LM.