Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection

Researchers introduce Budget-Sensitive Discovery Score (BSDS), a formally verified framework for evaluating AI-guided scientific candidate selection under budget constraints. Testing on drug discovery datasets reveals that simple random forest models outperform large language models, with LLMs providing no marginal value over existing trained classifiers.