Finding New Drugs Is Laborious. This AI Startup Is Skipping The Lab Work

Potential Sciences plans to build large-scale chemical datasets by using basic principles of physics to simulate interactions between atoms, then small molecules, then complex amino acids and proteins. With that data, it’s betting it can train AI models and prediction systems capable of identifying highly targeted drugs for specific diseases, bypassing costly and time-consuming lab experimentation.

“We want molecules that are never going to work in the clinic to fail fast,” Hatem Helal, Potential Science’s founder and CEO, tells WIRED. “That means having better predictors of how they will behave as they interact with human biology.”

The startup, which launched on November 5, says it has seed funding in the tens of millions of dollars from SovAI—the UK government’s AI venture capital fund—and Twin Path Ventures, among other investors.