This is like using a super-smart microscope that doesn’t look at proteins directly, but instead uses physics and patterns learned from millions of known proteins to "guess" the shapes of mysterious, previously unmeasurable proteins in our bodies.
A large fraction of human proteins—especially intrinsically disordered or hard-to-crystallize ones—remain structurally uncharacterized, limiting our understanding of disease mechanisms and slowing drug discovery. By combining AI with physics-based modeling, these tools aim to infer structures or structural ensembles for the "dark proteome," expanding the druggable target space and enabling more rational design of therapeutics.
Proprietary training data (large structural and biophysical datasets), integration of physics-based constraints with AI models, and tight coupling to downstream pharma workflows (target selection, hit discovery, and mechanistic studies).
Hybrid
Unknown
High (Custom Models/Infra)
Compute cost and latency for large-scale structural predictions and simulations, plus data curation and integration with wet-lab pipelines.
Early Adopters
Focus on the "dark proteome"—proteins that are disordered, flexible, or otherwise resistant to classical structural biology—by blending AI pattern recognition with explicit physics-based modeling and simulation, potentially capturing dynamic conformational ensembles rather than single static structures.