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 \textbf{expRxRec}. Furthermore, we introduce \textbf{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 release\footnote{A copy is attached with this submission} both the dataset and KEDRec-LM.

Due to the increasing of scientific publication, scientific information recommendation has become an urgent problem which can save retrieval cost. There are kinds of information that can be recommended, such as paper recommendation (Mei et al., 2022), author recommendation (Alhoori & Furuta, 2017), journal recommendation (Gündoğan et al., 2023) and so on. Among them, citation recommendation has arisen researchers' attention, which aims to help people find appropriate and necessary work to cite based on the given user queries. This paper aims to improve citation recommendation by considering the argumentative zoning of the citing sentence. Normally, authors will follow a logical framework when writing scientific papers. For example, the International Committee of Medical Journal Editors (ICMJE) recommends the IMRaD (Introduction, Methods, Results and Discussion) structure in writing and editing guidelines of biomedical publications (Editors & others, 2004). The structure of a research article is designed to present the research work clearly and concisely. This structure also helps to make it easy for readers to understand and evaluate the research.