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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
What Would Have to Change

What Success Would Actually Look Like

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The definition on this page is deliberately strict, and it is worth seeing why each part is there. An approved medicine matters because approval is the point where the evidence has been examined by people with no stake in the result. A patient-relevant benefit matters because a molecule can change a biological marker without helping anyone feel or live better, and that distinction is where a lot of candidates quietly disappear. And the third part, that AI did work that would have been hard to match otherwise, is what separates a genuine test of the promise from a molecule that happened to pass through a computational tool on its way to being made by chemists. Notice what is not on the list: a molecule entering a trial. That is an entry, not an outcome, and entries are cheap.
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Visible success means an approved medicine with a demonstrated patient-relevant benefit, whose design depended substantially on AI in a way that would have been hard to match by other means. A molecule entering trials is not success; it is entry into the filters.

Why counting molecules misleads

The number of AI-originated molecules entering trials can grow quickly, because entering is cheap relative to everything that follows. The number that reach patients grows only when candidates survive every filter. Watching entries therefore tells you about activity, not about impact. The informative quantity is the survival rate through each filter and whether it has changed.

The word "AI-assisted" covers a wide range

A model proposing a new molecular scaffold and a model ranking existing compounds are both computational contributions, but they test different claims. When you read that a drug is AI-designed, the meaningful question is what the model actually decided, and whether a human team could have reached the same place at comparable cost.

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