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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–8 of 8 filtered models
Atomic-level refinement of RNA 3D structures, using geometric attention networks to guide physics-based Monte Carlo sampling and L-BFGS optimization.
All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.
RNA-protein complex refinement via diffusion, repositioning the protein against the RNA to improve AlphaFold 3 and ProRNA3D-single backbones.
Backmapping model that rebuilds all-atom protein and nucleic acid structures from coarse-grained beads and inpaints unresolved residues.
Machine-learned interatomic potential supplying quantum-quality geometric restraints for refining cryo-EM and X-ray protein structures in Phenix.
Refines AlphaFold2 predictions against cryo-EM, cryo-ET, and X-ray data by optimizing coevolutionary embeddings rather than atomic coordinates.
Refines the CDR loops of a predicted antibody structure with SE(3) flow matching, steered at sampling time by bond, angle and torsion potentials.
Protein model accuracy estimation predicting per-residue lDDT plus signed residue-pair distance errors that become Rosetta refinement restraints.