The high failure rate of drugs in human trials after passing animal tests stems from a fundamental biological reality: a "mouse is not a small human." This "structural mismatch" is especially severe for modern, human-specific therapies like CAR-T and RNA, rendering animal models poor proxies.

While AI excels at screening vast compound libraries for potential drug candidates, it cannot overcome the ultimate bottleneck: the messy, complex, and poorly documented reality of human biology. The need for physical clinical trials remains the fundamental constraint on medical progress.

The high failure rate in drug development is analogous to trying to repair a car with no mechanical knowledge—it's just "banging on different parts." This highlights the industry's need to shift from observing correlations to understanding the fundamental biological mechanisms of disease.

19 of 20 drugs fail because diseases like cancer and Alzheimer's require complex solutions, not a simple "turn one screw" fix. Massively scaled human tissue testing allows for exploring the vast search space needed to find these multi-target cures, which traditional methods cannot handle.

The fear of toxicity pushes many companies to pursue the same few well-validated targets, leading to an average of nine assets per target. This hyper-competition not only crowds the market but, more importantly, leaves vast patient populations without effective options because their diseases lack these "popular" targets.

Despite AI's power, 90% of drugs fail in clinical trials. John Jumper argues the bottleneck isn't finding molecules that target proteins, but our fundamental lack of understanding of disease causality, like with Alzheimer's, which is a biology problem, not a technology one.

Designing therapeutics with immense combinatorial complexity is impossible through rational design alone. The optimal approach is to first use human biological hypotheses to narrow the vast search space. Then, employ large-scale screening and data analysis to optimize within that constrained space, navigating variables too complex for human comprehension.

Despite major scientific advances, the key metrics of drug R&D—a ~13-year timeline, 90-95% clinical failure rate, and billion-dollar costs—have remained unchanged for two decades. This profound lack of productivity improvement creates the urgent need for a systematic, AI-driven overhaul.

The primary bottleneck in drug development isn't creating therapies but identifying the right targets. Regeneron built its massive genetics database to find rare, protective genetic mutations in humans, effectively de-risking the target identification process and aiming to improve the industry's low success rate.