How a Google DeepMind Spinoff Hunts Hidden Drug Targets
TL;DR
For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery. Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard.
Nauti's Take
The hard part is not the demo, it is the biology after the demo. Isomorphic is working where AI has to do more than produce pretty protein pictures: sharper hypotheses, fewer dead lab loops, clearer binding logic.
For builders, that is the bar: model output only matters when it makes expensive experiments smarter.
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