Many effective drugs that are already developed will not reach patients for years because the clinical trial system is the primary bottleneck. This delay is due to logistical and structural inefficiencies in testing, not a lack of scientific discovery.

AI's initial pharma application focused on molecule design, driven by commercial incentives. New molecules are patentable and thus more easily fundable. This occurred despite incorrect target selection being the primary cause of drug development failure, representing a less immediately commercializable but more fundamental problem.

Progress in drug development often hides inside failures. A therapy that fails in one clinical trial can provide critical scientific learnings. One company leveraged insights from a failed study to redesign a subsequent trial, which was successful and led to the drug's approval.

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.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

The catastrophic failure rate in drug development isn't just bad luck; it's a structural problem. It originates from the very first decision: researchers, biased by existing literature and simplistic models, fixate on a single biochemical target, ignoring the body's complex, multi-faceted nature.

Despite the buzz, a clinical development expert cautions that AI's impact in drug development is limited. The primary bottleneck isn't the algorithms but the lack of sufficient, high-quality human biological data that can be translated into reliable predictions, as animal models often fail to provide it.

The process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.