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 reflected expert labeling characteristics, which facilitates 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.

The Black Death in the 1300s broke the long-ingrained feudal system in Europe and replaced it with the more modern employment contract. A mere three centuries later, a deep economic recession -- thanks to the 100-year war between England and France -- kick-started a major innovation drive that radically improved agricultural productivity. Fast forward to more recent times, the SARS pandemic of 2002-2004 catalyzed the meteoric growth of a then-small ecommerce company called Ali Baba and helped establish it at the forefront of retail in Asia. This growth was fueled by underlying anxiety around traveling and human contact, similar to what we see today with Covid-19. The financial crises of 2008 also produced its own disruptive side effects.