Closing the data loop in AI-driven drug discovery
How closing the feedback loop is revolutionizing pharmaceutical R&D and combating Eroom's Law.
Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today,...
MIT Technology Review
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Closing the data loop in drug discovery allows AI models to learn from experimental failures in real-time, drastically reducing the iterative cycle of pharmaceutical development. By integrating predictive analytics with laboratory feedback, researchers can pivot away from dead-end compounds before incurring massive financial losses.
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