arXiv:2505.23823v2 Announce Type: replace 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 4,4
The proliferation of LLMs and RAG frameworks has created a need for specialized benchmarks to validate their efficacy in complex scientific domains like drug discovery.
This benchmark addresses a critical gap in assessing AI's capability for accelerated drug discovery, which has significant implications for pharmaceutical R&D.
The existence of RAGPPI allows for more effective evaluation and improvement of AI models in identifying protein-protein interaction impacts, potentially speeding up target identification.
- · Pharmaceutical R&D
- · AI in drug discovery
- · Biotech companies
- · Patients
- · Traditional drug discovery methods
- · Inefficient AI models
Improved RAG models will accelerate the identification of drug targets, leading to faster initial stages of drug development.
Faster target identification could reduce drug development costs and increase the success rate of drug candidates.
A more efficient drug discovery pipeline may lead to a higher volume of novel treatments for various diseases, impacting global health outcomes.
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