Drug discovery remains a high‑cost, high‑risk field, with development costs doubling every nine years under Eroom’s Law. Bringing a new drug to market now averages 10–15 years and costs between $1 billion and $2.5 billion, with failure rates above 90 abaturage. AI is the industry’s biggest bet to raise success rates and cut timelines.

Early AI use focuses on hit identification, moving from empirical screening to predictive design. Instead of physically testing vast libraries, companies now generate candidates in silico, predicting interactions with disease targets before committing to R&D. This shifts the bottleneck from screening to detailed lab validation, demanding higher‑throughput, data‑rich technologies.

A critical hurdle is data quality. Many AI models trained on public datasets hit a “data wall” because they lack negative results and comprehensiveecycle information. 4% of biomedical papers contain manipulated images, and the rise of generative AI amplifies fabrication risks. Tools like Cytiva’s Image Integrity Checker use blockchain‑style hashing to flag tampering.

The vision is fully autonomous labs—dark labs that cycle prediction, testing, and optimization, feeding results back into AI models. Success hinges on interoperable systems, FAIR data, and integrated infrastructure to close the AI‑wet‑lab loop.