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Why AI-Designed Drugs Haven't Changed Medicine Yet

1The Promise and the Puzzle2From Molecule to Medicine: The Journey a Drug Must Survive3Where AI Actually Helps in the Pipeline4The Prediction Gap: When a Good Molecule Meets a Real Body5The Long, Expensive Road of Clinical Trials6Money, Incentives, and the Business of Drug Development7Regulation, Evidence, and Trust8What Would Have to Change
The Long, Expensive Road of Clinical Trials

Most Candidates Never Reach the Finish Line

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Watch the funnel narrow as it moves downward. The wide top is the many compounds that enter early testing. Most of them drop out here, during laboratory and animal work, because they fail basic safety or efficacy checks. That is the cheap end: losing a candidate here costs relatively little. Now follow the funnel to the narrow bottom. Very few candidates reach late-phase trials, but that is where the largest losses occur. When the animation highlights a late-stage failure, notice what has already been spent: years of salaries, manufacturing, site payments, and monitoring, plus a decade of the patent clock. And there is a third cost that is easy to miss. A late failure is often ambiguous. It might mean the drug does not work, or it might mean the trial was poorly designed or enrolled the wrong patients. That ambiguity makes it hard to decide whether to abandon the approach or try again. So the funnel is not just about numbers. It shows that the cost of being wrong rises sharply as you move down.
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Attrition is the steady loss of drug candidates as they move through development, and it is severe. For every compound that enters early testing, only a small number reach human trials at all, and of those, only a fraction complete the full sequence to approval. The losses are not evenly distributed. In terms of raw count, most candidates are eliminated early, during laboratory and animal testing, because they fail basic safety or efficacy checks. But in terms of cost and time, the losses that matter most happen late.

A failure in a late-phase trial is damaging for three reasons. First, the money already spent is gone: years of salaries, manufacturing, site payments, and monitoring cannot be recovered. Second, the time is gone: a decade may have passed, and the patent clock has been running the whole time, leaving less protected market time if the drug had succeeded. Third, the failure is often ambiguous. A late trial can fail because the drug truly does not work, or because the trial was poorly designed, enrolled the wrong patients, or could not measure the effect reliably. That ambiguity makes it hard to know whether to abandon the approach or try again differently.

This is why the funnel shape matters. The wide top represents many cheap early candidates; the narrow bottom represents few, very expensive late candidates. The cost of a mistake rises sharply as you move down, so the process is designed to be cautious precisely where it is most expensive to be wrong.

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