On this episode of The Startup Show, I spoke with Katie Kuo, CEO and co-founder of Atomistic Insights, a biotech startup working to speed up drug discovery using physics-based machine learning. Born out of Georgia Tech and currently part of the Alchemist Accelerator, Atomistic Insights is trying to fix a known problem: new drugs take too long and cost too much. Think $2.6 billion and up to 15 years per FDA approval.

Katie’s background is in chemistry and drug discovery, and the company’s approach is refreshingly different. Instead of treating proteins like static objects, their system tracks how they actually move—atom by atom—in the body. That motion reveals hidden binding sites for drugs, which traditional drug design often misses. It’s like watching a lock move in slow motion until you see the exact shape of the keyhole.

Their method is already patented. The team has a working MVP. They’re raising their first round. And their goal is to go beyond software licensing to actually develop in-house therapeutics—taking their discoveries all the way to the clinic.

What makes Atomistic different isn’t just the tech. It’s the founder mindset. Katie isn’t chasing AI hype. She came from a lab, saw how slow and expensive drug discovery is, and wanted to do something about it. This is a startup driven by application, not abstraction.

So, what’s next? More partnerships, deeper simulations, and eventually, real drugs designed faster and cheaper using Katie’s system. Atomistic isn’t trying to replace drug discovery. They’re just trying to make it suck less.

You can learn more at atomisticinsights.com (or at least try to spell it correctly in your search bar).