Cytiva said AI drug discovery is being held back by weak data quality and poorly integrated lab systems, even as the industry looks to cut 10-15 year development timelines and $1 billion-$2.5 billion costs.
AI is helping companies move from physical screening to predictive design, generating more candidate hits earlier, but those compounds still need lab validation because models cannot yet reliably predict kinetics or developability.
Public datasets are becoming a bottleneck because they lack structure, diversity and negative results, while publication bias leaves failed experiments buried and limits models' ability to learn what not to pursue.
Data integrity is a second risk: Cytiva cited research finding nearly 4% of biomedical papers contained duplicated or manipulated images, and pointed to image-checking tools using secure hashes to detect tampering.
Cytiva sees autonomous 'dark labs' as the longer-term goal, but says most labs still run standalone instruments; no primarily AI-designed drug has full FDA approval yet, though it expects that within 2-3 years.