From Data Factory to Better Drugs: Part 4 - Validating Hypotheses at the Bench

"A hypothesis from a model isn't a discovery yet. So, it goes back into the lab. The biology the model predicted is the biology we saw at the bench." A computational prediction alone does not cure a disease. In Part 4 of our platform series, Associate Principal Scientist Celia Fernandez explains how Recursion closes the loop to turn AI generated hypotheses into validated biological targets. Once our foundation models point to a potential target, we run the lab in the opposite direction. The hypothesis enters a validation cascade that becomes progressively more demanding and highly specific to the disease. This physical testing allows us to aggressively derisk our computational discoveries directly at the bench. We test these targets against the exact same high bar that our global pharma partners hold their own internal programs to. When the loop finally closes, we can confidently prove that the biology the machine learning model predicted matches the physical reality we see in the lab. 🚀Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AIdrugdiscovery #AI #drugdiscovery