PharmaBench: Enhancing ADMET benchmarks with large language models

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing benchmark sets have limited utility for AI modeling due to small dataset sizes and a lack of representation of compounds. To address this issue, we propose a multi-agent data mining system based on Large Language Models to effectively identify experimental conditions within 14,401 bioassays. This assists in merging entries from different sources, resulting in the creation of PharmaBench. This collection includes eleven datasets and 52,482 entries.