physics-valid generative AI · for drug discovery
Distribution-aware conformer generation.
Docking-ready.
Better 3D conformer ensembles than RDKit ETKDG. Physiological-pH protonation. Macrocycle mode. Ring pucker diversity. One API.
Built for computational chemistry teams at biotech and pharma. Compact diffusion arch — 60% of drug-space coverage today, on par with commercial tools at a fraction of the price.
PRODUCT · MEASURED
Physon.Molecular
Distribution-aware conformer generation for drug discovery.
Compact diffusion model produces 3D conformer ensembles for small molecules. Beats RDKit ETKDG on distribution shape (Bhattacharyya overlap up to 0.888) at a fraction of the sampling cost.
PRODUCT · CATEGORICAL
Physon.3D
Physics-valid 3D scene generation for world models and simulation.
Predicts 3D voxel scene evolution from a partial context. On our benchmark, a standard ConvLSTM3D baseline scored 0.0000 IoU — couldn't learn 3D temporal at all. Physon.3D scored 0.3244.
Built on physics-first principles
Modern generative AI treats physics as an inconvenient constraint. Physon treats it as the operating substrate. Every generated output respects the symmetries, invariants, and structural laws of the underlying scientific domain.
Symmetry-preserving
Outputs respect the invariant structures of the target physics — not enforced after training, but built into the architecture.
Compact
Small parameter counts. Runs on modest hardware. No trillion- parameter LLM required to respect physics.
Distribution-aware
Not just point predictions — ensembles that match the true statistical structure of physical states.
Cross-modal
One architectural family serves 3D scenes, molecular structures, and gauge field configurations.
Constraint-native
Antisymmetry, unit-norm manifolds, gauge invariance — architectural, not trained.
Rooted in tradition
Design principles draw from centuries of mathematical traditions — both modern and classical.
The specific architectural mappings are proprietary.