Papers by Youngseung Jeon
GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration (2025.findings-naacl)
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| Challenge: | Large Language Models and Retrieval-Augmented Generation frameworks have accelerated drug discovery, but integrating models into workflows remains challenging. |
| Approach: | They propose a large-scale knowledge graph-based retrieve-divide-solve agent pipeline RAG framework to support large-level PPI signaling pathway exploration. |
| Outcome: | The proposed framework is based on large-scale knowledge graphs and can be used to analyze protein-protein interactions. |
RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery (2026.eacl-long)
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| Challenge: | Large Language Models and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID, but no benchmark exists for identifying biological impacts of PPIs. |
| Approach: | They propose to build a factual question-answer benchmark of 4,420 question-announced pairs that focus on the potential biological impacts of PPIs. |
| Outcome: | The proposed benchmark is based on 4,420 question-answer pairs with expert-driven data annotation. |