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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 1102 filtered models
T cell receptor foundation model that scores peptide-MHC-TCR recognition and generates pMHC-conditioned CDR3β candidates from one shared backbone.
All-atom de novo protein design that denoises sequence and structure as a single Cartesian process, reading residue identity from atomic occupancy.
Anticancer peptide prediction specialized to breast cancer, running an MLP head over frozen ProtBERT embeddings of short peptide sequences.
Plasma proteomics foundation model for prospective disease risk prediction across 144 diseases. Unmeasured proteins are omitted rather than imputed.
Peptide-HLA class I binding predictor that deconvolves multi-allele immunopeptidomics, pairing frozen ESM-2 embeddings with attention-based MIL.
Antibody language model that reads all four frameworks as a prompt and generates CDR1-CDR3 jointly, with weighted-DPO alignment to design rewards.
Phylogeny-informed transformer that detects episodic diversifying selection in codon alignments, amortizing maximum-likelihood dN/dS tests.
Protein language model that grows its own depth and width during masked-language pretraining on the human proteome, reaching 76.0% Pfam-50 accuracy.
Antibody-antigen binding affinity predictor that scores the interface as a graph over frozen ESM-2 embeddings, jointly regressing log10(KD) and ΔG.
Shared MHC class II neoantigen design for breast cancer, pairing retrieval-augmented ProtT5 embeddings with an MCNN-BiLSTM immunogenicity classifier.
Protein dynamics prediction from one equilibrium structure, giving per-residue directional flexibility and pairwise residue couplings.
Spatial RNA-to-protein prediction turning transcriptomics-only sections into virtual protein maps via two adaptively reweighted graph channels.
Self-supervised encoder for tandem mass spectra that learns proteomics representations from 1.63 billion MS/MS scans with no peptide labels.
Pocket-conditioned 3D ligand generation for structure-based drug design, with variance-exploding diffusion that keeps generated geometry low-strain.
Structure-based protein-RNA screening that reads position-wise nucleotide preferences off a single complex structure and ranks libraries in seconds.
Capsid-specialized protein language model that finds divergent viral capsid proteins in metagenomes and encodes shell architecture from sequence.
Protein inverse folding by Dirichlet flow matching, refining the whole sequence at once with fixed-residue inpainting and soft residue priors.
Sequence-only protein-RNA interaction model that predicts binding affinity, maps residue- and nucleotide-level interfaces, and designs binding RNAs.
All-atom diffusion model for joint protein, peptide, nucleic acid and ligand complex prediction, scored on interface accuracy and steric validity.
Protein-sequence-conditioned mixture-of-experts language model that designs small molecules and emits an explicit synthesis route for each one.
Drug repurposing model that propagates transcriptional signatures through a pretrained graph autoencoder of the human protein interaction network.
Per-residue intrinsic disorder prediction from sequence, with a window-wide output encoding that lets a convolutional network span 91 residues.