Can AI Reveal Hidden Drug Targets in “Undruggable” Proteins?

Quantibody.ai leads the field in cryptic pocket discovery, using AI and molecular simulations to find hidden binding sites on disease-relevant proteins. These cryptic or allosteric pockets are often invisible in static crystal structures but represent high-value, selective drug targets.

Our models analyze protein flexibility, molecular motion, and transient pocket formation using deep learning and MD simulations. We uncover targetable cavities that enable the design of allosteric modulators or non-competitive inhibitors.

This opens the door to previously “undruggable” proteins like transcription factors, scaffold proteins, or disordered targets. Our cryptic site detection engine integrates seamlessly with ligand generation, allowing us to rapidly design molecules that fit these novel pockets.

By expanding the druggable proteome, we empower pharma and biotech innovators to tackle complex diseases with new mechanisms of action. Cryptic sites also offer opportunities for greater specificity and fewer side effects.

Quantibody.ai enables structure-based drug discovery far beyond conventional limits. Discover what others miss—by design.