A new perspective argued that AI-driven drug discovery depends on trustworthy biological data—specifically requiring authenticated datasets, validation, and standardized metadata. The piece frames dataset provenance and consistency as preconditions for reliable model training and for translating outputs into candidate generation. The authors emphasize that without standardized metadata and documented validation, biological measurements can introduce systematic errors that propagate into AI predictions. The argument targets practical implementation issues seen across translational pipelines, where experimental variability and incomplete labeling can undermine downstream use. For biotech teams scaling AI across discovery, the report’s core message is that the limiting factor may be data governance rather than model architecture, setting expectations for how internal and external datasets should be curated.