Post by Imperagen

AI is transforming life sciences R&D, but only when it is connected to the right data. In a new article for TechFinitive.com, Imperagen CEO Guy Levy-Yurista argues that many AI-guided drug and molecule development efforts fall short because they rely on generic, sparse, or publicly available datasets that were never built for the specific problem at hand. Predictions that may look impressive in silico, but fail when tested in the lab.  At Imperagen, we believe the answer is a closed-loop approach: generate the data yourself, own the data-generation layer, and connect every wet lab experiment directly back into the computational model. That means:  • Starting with physics-based, atomistic models of the enzyme and substrate  • Screening mutation combinations computationally before experiments begin  • Testing thousands of variants in the lab  • Using data that comes not only from successes, but the failures as well, so the model learns the true boundaries of what works  • Feeding every measured result back into the next round of prediction This creates a compounding effect: each cycle makes the model smarter, more specific, and more predictive for the exact enzyme, reaction, and conditions being engineered. For life sciences R&D, this marks a shift from using labs as validation facilities to treating them as data-generation engines. Every experiment becomes an investment in model intelligence. The promise of AI in life sciences is real as long as we solve the data challenge.