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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 97–120 of 500 filtered models
Multimodal protein pre-training framework jointly learning sequence, 3D structure, and surface representations via implicit neural representations.
All-atom protein structure diffusion models for motif scaffolding and hotspot-conditioned complex generation, at 22M parameters.
Open-source reproduction of AlphaFold 3 that predicts structures of proteins, DNA, RNA, and small-molecule ligands, including their mixed complexes.
Structure prediction for protein, RNA, and protein-RNA complexes in one AlphaFold2-derived framework that accepts MSA or language model encoders.
Cyclic peptide design conditioned on target protein structure, generating all four cyclization types via all-atom, all-bond harmonic SDE modeling.
Protein-nucleic acid complex structure prediction from sequence, folding protein, DNA and RNA chains in one network with confidence estimates.
Sequence-to-ensemble predictor that generates conformational ensembles of intrinsically disordered proteins zero-shot, with no per-sequence refitting.
Protein backbone design model pairing flow matching with a Mamba state-space backbone, generating long proteins in linear time with exact geometry.
Protein structure prediction for monomers and complexes in one three-track network, scaling past 1000 residues without triangle attention.
Multi-task protein framework recasting function, binding site, and structure prediction as autoregressive next-token prediction over ESM2 embeddings.
Retrieval-augmented diffusion model for protein inverse folding that conditions sequence generation on profiles from structurally similar homologs.
AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.
Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.
Protein-conditioned RNA sequence and structure co-design, refining flow-matched backbones against Lennard-Jones and folding free-energy terms.
Protein structure prediction from general-purpose transformer blocks and flow matching, with no MSAs, pair representations, or triangle attention.
Protein model accuracy estimation for single chains and complexes, predicting per-residue lDDT, interface QS-score, and overall fold TM-score.
All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.
Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.
Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.
Lightweight AlphaFlow variant that fine-tunes only AlphaFold's structure module, keeping the Evoformer frozen to cut conformational sampling cost.
Multi-modal protein language model using the MSA evolutionary profile as a reasoning step between structure and sequence. 650M outperforms ESM-3 1.4B.
Multitask binding site prediction across protein, DNA/RNA, ligand, lipid, and ion partners, combining protein language models with equivariant GNNs.
Unified atomic diffusion model for protein structure prediction and de novo antibody design, steered by epitope and target-structure constraints.