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arXiv · 2505.23823

RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery

Youngseung Jeon·Ziwen Li·Thomas Li·JiaSyuan Chang·Morteza Ziyadi·Xiang 'Anthony' Chen
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Abstract

Retrieving the biological impacts of protein-protein interactions (PPIs) is essential for target identification (Target ID) in drug development. Given the vast number of proteins involved, this process remains time-consuming and challenging. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID; however, no benchmark currently exists for identifying the biological impacts of PPIs. To bridge this gap, we introduce the RAG Benchmark for PPIs (RAGPPI), a factual question-answer benchmark of 4,420 question-answer pairs that focus on the potential biological impacts of PPIs. Through interviews with experts, we identified criteria for a benchmark dataset, such as a type of QA and source. We built a gold-standard dataset (500 QA pairs) through expert-driven data annotation. We developed an ensemble auto-evaluation LLM that incorporates expert labeling characteristics, average fact-abstract similarity (F1), and low-similarity fact counts (F2), enabling the construction of a silver-standard dataset (3,720 QA pairs). We are committed to maintaining RAGPPI as a resource to support the research community in advancing RAG systems for drug discovery QA solutions.

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Youngseung Jeon, Ziwen Li, Thomas Li, JiaSyuan Chang, Morteza Ziyadi, Xiang 'Anthony' Chen. 2026-06-11. RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery. https://doi.org/10.18653/v1%2F2026.eacl-long.203

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