TriaX is a tensor-trained foundation model that represents proteins in full conformational flexibility across their entire interaction surface. One generalized model for protein–protein interaction modulation and induced-proximity design — the molecular glues, degraders, and modulators that work by controlling which proteins come into contact.
Three axes — all flexibility, all spatial coverage, and induced proximity — trained into one tensor model, so a single system speaks the grammar of protein interaction across targets it has never seen.
Protein–protein interactions drive most of biology, and most of them remain undruggable. Their interfaces are broad and flat, their binding sites appear only when the protein moves, and the newest therapeutics don't block a target at all — they hold two proteins together. Conventional tools, built on static structures and known pockets, miss all three of these realities. TriaX was built to model them directly. The name is literal: three axes of the same problem — flexibility, spatial coverage, and proximity — learned together in one model rather than bolted on one at a time.
Most models pick one simplification — a rigid structure, a known pocket, a single target. TriaX refuses all three. Each axis feeds the same shared representation, so gains on one carry to the others.
Proteins are ensembles, not snapshots. TriaX represents the full conformational landscape — including transient and cryptic states — so interactions are scored against how a protein actually moves, not one frozen structure.
Interaction interfaces are large and historically undruggable. TriaX models the entire spatial surface of a protein rather than a handful of known pockets, surfacing modulation sites across the whole interface.
Proximity drugs work by holding two proteins together. TriaX models the full ternary complex — target, effector, and the molecule between them — to design glues and degraders, not just single-target binders.
TriaX learns a shared language of protein interaction that generalizes across targets. The same model proposes modulators and proximity-inducers for interactions it has never encountered, instead of being retrained from scratch for every new program — the difference between a tool per target and a foundation for the whole field.
Tell us about your interaction of interest — the target class, the modality you're after, where the program is today — and we'll follow up with next steps for early access.