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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 505–528 of 1102 filtered models
Peptide-HLA immunogenicity prediction with a BiLSTM ensemble, inside a pipeline that finds microbial epitopes mimicking tumor neoantigens.
Text-guided protein editing framework with disentangled structure and function latents, edited by rewriting either description at inference time.
Structure-based drug design model pairing SE(3)-equivariant diffusion with retrieval of pocket-matched scaffolds to generate ligands for a target.
MSA design model generating alignments from protein language model embeddings to improve folding accuracy on orphan and low-homology proteins.
Antibody language model pretrained on 402 million OAS sequences, matching far larger antibody LMs on repertoire tasks at 125M parameters.
De novo peptide sequencing from tandem mass spectra, using curriculum learning and iterative self-refinement to stabilize non-autoregressive decoding.
Protein-protein binding affinity and interface hotspot prediction from sequence alone, using protein language models fine-tuned on SKEMPI 2.0.
Discrete diffusion model for protein sequence design in an all-atom SELFIES representation, reaching non-canonical and modified amino acid residues.
Multimodal viral foundation model over nucleotide and protein sequence, built for virus discovery, function annotation, and antibody design.
Viral protein language model that predicts a virus's animal host from one protein sequence, generalizing to rare and unseen hosts at 18M parameters.
Diffusion model that co-designs binder sequence and backbone for arbitrary protein targets, pretrained on 706,360 protein-protein complexes.
Proteome-scale protein dynamics prediction from sequence or structure, predicting residue flexibility, correlations, and conformational states.
Ligand-binding protein design driven by a natural-language function description plus a ligand SMILES string, in 1B and 3B parameter variants.
Inverse folding framework combining a Markov bridge generator with direct preference optimization to design low-energy sequences and predict ΔΔG.
Protein fitness prediction with end-to-end differentiable homology search, replacing MSA construction with vector search over 62M UniRef50 sequences.
Protein language model trained with masked diffusion, unifying representation learning and generative design in one 650M-parameter model.
Inverse folding model refined by online reinforcement learning against folding and stability rewards, cutting design failure rates by 36-48%.
Virtual drug screening from per-atom protein and ligand embeddings retrieved by nearest neighbors. 30.4 EF1% on DUD-E at ~14 s per million molecules.
Protein function annotation model predicting Gene Ontology terms with direct preference optimization layered on a frozen ESM-C sequence encoder.
Diffusion model that backmaps coarse-grained protein structures to all-atom detail, scaling to condensates of over a million residues.
Structure-based virtual screening model that scores ligands against apo and predicted pockets, lifting blind-apo EF1% on DUD-E from 11.75 to 37.19.
Domain-level language model treating Pfam protein domains as tokens to predict and design bacterial and fungal biosynthetic gene clusters.
RNA-protein contact prediction from sequence, built on ERNIE-RNA and ESM-2 embeddings. Reaches 0.77 auROC where AlphaFold 3 reaches 0.61.
Relative protein-ligand binding affinity prediction from docked complexes, matching Schrodinger FEP+ ranking accuracy zero-shot on the FEP benchmark.