Drug programs don't fail at the clinic. They fail at the hypothesis.
Two in every three late-stage failures trace back to incorrect target selection. The evidence was there. It just never surfaced.
No keyword search knows what you believe, what stage you're at, or what evidence would change your mind.
One missed signal can kill a $2B program.
Evidence is scattered across millions of papers, trial results, FDA filings, and patents — unstructured, spread across 80+ sources, growing every day.
Missing one piece of evidence doesn't just slow a program. It sends it back to zero.
AUO is a living workspace where hypotheses evolve with evidence.
Discovery moves in branches. Most tools treat it as a straight line. AUO treats every hypothesis and every piece of evidence as an object — monitored continuously across scientific literature, clinical trials, and regulatory filings.
Bring your work — papers, datasets, grant briefs. AUO understands the hypothesis, the target, the constraints, and the current state of belief. It monitors continuously across PubMed, ClinicalTrials.gov, openFDA, patents, and regulatory filings. When a signal crosses a threshold, AUO proposes the right next step: narrow, pivot, split, or kill. The team decides. AUO learns.
The more it is used, the more irreplaceable it becomes.
Every stance on evidence, every pivot, every kill becomes structured intelligence. AUO learns the context of the program, the reasoning patterns of the team, and the history of every hypothesis the organization has ever run.
The record grows. Context compounds. Every new hypothesis builds on it. The whole team builds on the same layer of truth.
After two years of use, switching is not a commercial decision. It is an epistemic one.
Three structural shifts converging.
LLMs make context-aware extraction possible. Scanning millions of papers and FDA documents — and understanding what each piece of evidence means for a specific hypothesis — was not economically feasible three years ago. It is now.
The failure data is finally public. FDA Complete Response Letters — detailed rejection documents explaining why programs failed — became publicly available in July 2025. The full corpus needed to learn from hypothesis failures now exists.
R&D productivity is at a historic low. $288B invested in 2024. 90% failure rate unchanged for decades. 40–50% of failures traced to wrong target — a hypothesis problem, not a chemistry problem.
The window is open now. The data exists. The infrastructure exists. The pain is acute.
Wrong hypotheses cost years. They cost billions. Most importantly, they cost patients.
Better hypotheses reach the clinic. Better drugs reach patients. That is the mission.