Closing the data loop in AI-driven drug discovery
TL;DR
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, bringing a new drug to market takes an average of 10-15 years and costs….
Nauti's Take
A small team should first test whether its experiments, failures, and measurement data can return to the development process in a consistent format. An AI model adds little value when critical data sits in separate systems or results are poorly documented and hard to reproduce.
Before funding a large model project, run the loop on a limited drug program and measure whether it actually shortens decision cycles.
Summary
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, bringing a new drug to market takes an average of 10-15 years and costs…
Sources