Instead of stopping a trial early for success—which regulators may restrict—a high-confidence early signal provides immense value as business intelligence. This allows sponsors to de-risk and accelerate planning for subsequent phases months earlier, creating millions in value by reducing the gap between Phase 2 and 3.

The long-term strategy for AI in drug discovery is a two-step process. First, create an AI platform to design effective drugs. Second, after a dozen or so AI-designed drugs succeed, use that data to convince regulators to trust AI predictions, potentially allowing future drugs to skip steps like animal testing and accelerate trials.

Beyond early discovery, LLMs deliver significant value in clinical trials. They accelerate timelines by automating months of post-trial documentation work. More strategically, they can improve trial success rates by analyzing genomic data to identify patient populations with a higher likelihood of responding to a treatment.

The $5 billion cost to develop a drug is primarily driven by the high failure rate (9 out of 10) in late-stage trials. AI's biggest financial impact will be predicting which drugs will succeed, drastically reducing wasted R&D. This efficiency is what will ultimately make drugs more affordable.

AstraZeneca's CEO, Pascal Soriot, is focusing AI on optimizing expensive late-stage clinical trials to improve success rates. This strategic shift from the common focus on early-stage discovery targets the area of greatest expense in drug development. He also downplayed AI's impact on jobs, calling the idea a 'fake story' and aiming to make people 'faster and smarter.'

AI is delivering tangible results now. An internal Eli Lilly study showed that using an AI-enabled triaging workflow for developability and structural diversity in early discovery has significantly reduced the number of 'surprises' and liabilities for molecules entering later development stages.

While most focus on AI for drug discovery, Recursion is building an AI stack for clinical development, where 70% of costs lie. By using real-world data to pinpoint patient locations and causal AI to predict responders, they are improving trial enrollment rates by 1.5x. This demonstrates a holistic, end-to-end AI strategy that addresses bottlenecks across the entire value chain, not just the initial stages.

The highest-value application of AI in clinical development is in the design phase. By simulating trial outcomes with historical data and virtual patient cohorts, companies can identify and resolve potential bottlenecks, like flawed protocols or recruitment issues, before committing massive resources. This

While AI for novel drug discovery has lofty goals, its most practical value lies in accelerating development. This includes applying AI to de-risked assets for new indications, improving delivery methods, and designing faster, more effective clinical trials, which is where the real bottleneck lies.