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    CURRENT RESEARCH

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    Mechanistic Understanding and Prediction of Drug Interactions in Humans using LLM-based Explainable Multimodal Framework

    - To enable the prediction of drug interactions in humans based on the mechanistic understanding
    - To improve the explainability of the in-human prediction results using curated mechanistic explanations
    - To propose technical solutions to incorporate knowledge-graph instilled multimodal drug features and conformal prediction driven input selection strategy

    Risk of MASLD in Patients with Type 2 Diabetes Treated with GLP-1 Receptor Agonists Using Real-World Data

    - To evaluate the preventive effect of GLP-1RAs on MASLD development in patients with T2DM
    - To assess differences in MASLD incidence among diabetes blood-glucose–lowering treatment groups
    - To characterize real-world clinical practice, including treatment patterns and prescribing trends of GLP-1RAs and other antidiabetic agents

    Development of a Large-Scale Korean-English Corpus and a Document-Level Translation Model for Pharmaceutical Field

    - To build a large-scale Korean-English parallel and monolingual corpus specialized for the pharmaceutical field to reflect domain-specific terminology and maintain consistent translation quality
    - To conduct expert validation and ensure translation quality
    - To develop and enhance a -level neural machine translation model and a large language model specialized for the medical and pharmaceutical fields

    Improving generalization performance of Natural Language Procesing Models to extract drug safety information from multi-institutional clinical notes

    - To improve the generalizability of a natural language processing model in extracting adverse drug reaction signal from clinical notes.
    - To test the feasibility of using clinical notes generated in diverse clinical settings (e.g., inpatient, outpatient, practitioner specialty, ER visit, operating room) as a means to improve model’s generalizability.
    - To test the applicability of the large language models (e.g., GPT4) in improving model robustness.

    CCADD

    Center for Convergence Approaches in Drug Development, Graduate School of Convergence Science and Technology, Seoul National University

    Room C-208, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16229, SOUTH KOREA (Gwanggyo)

    Room 406, Building 17, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, SOUTH KOREA (Yeon-gun)

    Tel: +82-31-888-9189 (Gwanggyo); +82-2-3668-7381 (Yeon-gun)

    Fax: +82-31-888-9575

    Email: ccadd.snu@gmail.com