Talus Bioscience, Inc. (“Talus Bio”), an AI-native therapeutics company, released Ptarmigan-1, the first structure-free model that can accurately predict small molecule binding sites across the entire human proteome, including proteins too disordered for traditional 3D modeling to resolve. Access to Ptarmigan-1 is now available through a public portal.

While most AI drug discovery tools rely on fitting a molecule onto a defined protein pocket, approximately 40% of the proteome, including many disease-relevant transcription factors and regulatory proteins, are flexible and lack a 3D structure for therapeutics to grab hold. Ptarmigan-1 is designed to reach these targets by eliminating the structure requirement entirely.

“After the 2024 Nobel Prize in Chemistry, which recognized advances in protein structure prediction and design, scientists have increasingly focused on using structure to drive discovery,” said Alex Federation, PhD, CEO and Co-founder of Talus Bio. “With Ptarmigan-1, we now have a complementary approach for roughly half of all human proteins that have evaded drug discovery because they can’t be folded in a computer.”

When evaluating a recently disclosed STAT6 inhibitor series, a validated target for inflammatory disease with a disordered, hard-to-model binding site, Ptarmigan-1 outperformed structure-based methods in selecting successful drug candidates, despite never having seen the target or molecules in training. The model also identified novel small molecules that bound to the flexible pocket on STAT6. These candidates were validated in a third-party lab, demonstrating that Ptarmigan-1 can surface new chemistry from sequence alone.

Ptarmigan-1 is powered by MARMOT, Talus Bio’s proprietary proteomics platform, which measures proteins at work inside living human cells rather than in a test tube, opening the door to complex biology across oncology, autoimmune disease, and more indications.

By skipping protein folding, the model runs 5,000 times faster than structure-based methods, screening 3.4 billion compounds against all human proteins in a day to uncover cryptic pockets inaccessible to static structures.

“The data we’re building at Talus is structure-agnostic, meaning we can measure proteins whether or not they hold a fixed shape,” said Lindsay Pino, PhD, Chief Technology Officer and Co-founder of Talus Bio. “That means the model can learn just as well from flexible or intrinsically disordered proteins as it does from structured ones, which is what lets it generalize to targets nobody’s had a way to study before.”

“This is not only about reaching new targets, it is about accessing new chemistry,” said Gavin Hirst, PhD, Vice President of Small Molecule Drug Discovery at insitro and a Scientific Advisory Board member at Talus Bio. “Structure-based methods are strongest on chemotypes that already have a solved 3D complex, which quietly biases every campaign toward the chemistry we have already explored. Scoring without a pose breaks that coupling, and the speed of this model lets us test far more hypotheses, breaking us out of the cage of common chemistry.”