New Benchmark Evaluates AI Agents in Preclinical Drug Discovery

A new benchmark, TherapeuticsBench Preclinical Pharmacology (TxBench-PP), has been developed to assess the capabilities of AI agents in the critical area of small-molecule preclinical pharmacology. This benchmark aims to provide a verifiable evaluation framework for AI in drug discovery, focusing on whether agents can interpret real-world assay data and make sound program decisions, rather than relying on memorized information. TxBench-PP includes 100 evaluations covering various program stages, assay types, and task structures, such as mechanism-of-action reasoning, compound-target engagement, and safety assessments. Agents are presented with realistic workflow scenarios and must inspect files in a coding environment to provide structured answers. Initial evaluations across 16 model configurations, involving 11 different AI models, demonstrated that no system consistently achieved reliable preclinical pharmacology decisions. The top-performing configuration, Claude Opus 4.8 / Pi, passed only 59.3% of endpoint attempts, indicating significant room for improvement in AI's ability to handle complex drug discovery tasks.