Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
Using AI to fix millions of missing labels in drug-screening databases
Large language models can reliably fill in missing metadata for millions of bioassays in public databases, correctly identifying assay types and detection methods over 96% of the time. The researchers found that major repositories like PubChem are missing critical information on up to 89% of their assays—a gap that slows down AI drug discovery—and showed that LLMs can both complete these gaps and catch mislabeled entries that human curators might miss.
Drug discovery AI models depend on clean, complete data to work well, but the largest public bioassay databases have systematic gaps that currently require expensive manual labor to fix. This work shows LLMs can automate that curation at scale, potentially saving months of expert time while improving data quality. Better-labeled bioassay databases mean faster, more reliable computational drug screening and fewer dead-end experiments in the lab.