Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 457–480 of 1004 filtered models
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.
De novo peptide sequencing from tandem mass spectra, using curriculum learning and iterative self-refinement to stabilize non-autoregressive decoding.
Antibody language model pretrained on 402 million OAS sequences, matching far larger antibody LMs on repertoire tasks at 125M parameters.
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%.
Diffusion model that backmaps coarse-grained protein structures to all-atom detail, scaling to condensates of over a million residues.
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.
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.
Relative protein-ligand binding affinity prediction from docked complexes, matching Schrodinger FEP+ ranking accuracy zero-shot on the FEP benchmark.
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.
Codon optimization model for heterologous expression in E. coli, fine-tuning ProtBert to label each residue with an expression-weighted codon.
Antibody-antigen binding prediction from heavy chain, light chain, and antigen sequence, scoring 0.946 AUROC on a SARS-CoV-2 benchmark.