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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 Promise and the Puzzle

What an AI-designed drug actually is

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The key move here is separating design from everything else. A model can scan huge numbers of possible molecules and rank them by predicted properties, which is genuinely useful. But the molecule still has to be synthesized, tested in cells and animals, and eventually tested in people. None of that gets faster just because the molecule was chosen by a model. So when you hear 'AI-designed drug,' picture a different starting point, not a shortcut through the rest of the process.
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An AI-designed drug is a molecule whose structure was proposed or selected with the help of machine learning, then made and tested through conventional laboratory and clinical methods.

Design is one step, not the whole job

Machine learning is good at searching a very large space of possibilities and ranking candidates by predicted properties. That is the design step. Making the molecule, checking it in cells and animals, and testing it in people are separate steps that follow the same rules they always have.

A useful way to hold this: AI changes which molecule you start with. It does not change what that molecule must survive afterward.

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