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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 337–360 of 1004 filtered models
Protein language model that predicts which of eight lipid categories a protein binds from sequence alone, plus binding sites and mutation effects.
Linear B-cell epitope prediction from peptide sequence alone, pairing ProtT5 embeddings with an SVM trained on 222,030 curated IEDB peptides.
Gene Ontology prediction for malaria parasite proteins, trained on SAR supergroup structure embeddings with calibrated uncertainty and FDR control.
Antimicrobial resistance risk predictor using ESM2 embeddings of single protein mutations to flag resistance variants across bacterial pathogens.
Multimodal tokenizer for antibody CDR loops, encoding backbone dihedrals and sequence as discrete tokens that plug into antibody language models.
Protein-ligand binding site prediction that ranks pocket residues and pocket center coordinates, staying accurate on AlphaFold-predicted structures.
Peptide-MHC class I binding predictor that scores force-field energy terms from modeled pMHC structures, holding precision on rare HLA alleles.
Sequence-based multitask model predicting covalently ligandable cysteines and reversible ligand-binding residues across the human proteome.
Antibiotic resistance gene detection in metagenomes, pairing frozen ESM-1v embeddings with light classifier heads for drug class and mechanism.
Protein sequence-structure co-embedding model placing domains, full sequences, and short segments in one 32-dimensional contrastive space.
Structure-free peptide binder design conditioned only on a target protein sequence, using contrastive alignment to steer a latent diffusion model.
De novo drug design model generating target-conditioned ligands by latent diffusion over 1D SELFIES strings, conditioned on protein sequence alone.
Binding free-energy surrogate trained on 1.4M molecular dynamics frames, ranking docking poses ~28,000x faster than physics-based MMGBSA.
Generative protein language model that designs synthetic linear epitope libraries, and classifiers that filter them by bacterial or viral origin.
Protein-ligand interaction predictor that types seven contact classes between residues and ligand functional groups from sequence and SMILES alone.
Protein binder design model post-trained from a multimodal protein language model to bind proteins, peptides, small molecules, and nucleic acids.
Structure-based drug design model generating 3D ligands in a protein pocket with interaction-guided flow matching and a learned atom-count predictor.
Structure-based drug design model generating 3D ligands in protein pockets under gradient guidance for affinity, synthesizability, and selectivity.
Protein degrader design framework that mines fragment-target data to build PROTACs and predicts degradation potency (DC50) and maximal degradation.
Generative antibody model that produces light-chain sequences conditioned on a heavy chain, pairing a RoBERTa encoder with a GPT-2 decoder.
Antibody-antigen binding free energy predictor fusing ESM-2 sequence embeddings with persistent homology and interface geometry via cross-attention.
Energy-based flow matching for 3D molecular structure, using an idempotent predict-and-refine map for protein backbone generation and ligand docking.