Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 985–1008 of 1102 filtered models
Peptide tandem mass spectrum prediction across the full fragment ion series, with neutral losses and modification-specific peaks beyond b/y ions.
Antibody language model trained on paired and unpaired OAS sequences to suggest non-germline mutations instead of reverting them to germline.
Protein homology search via contrastive per-residue ResNet embeddings, acting as a pre-filter at least 5x faster than the one inside HMMER3.
Multimodal protein pre-training framework jointly learning sequence, 3D structure, and surface representations via implicit neural representations.
Structure-based drug design diffusion model that re-extracts the essential binding subcomplex from a pocket at every step of 3D ligand generation.
RNA-binding protein affinity prediction at single-base resolution from sequence alone. One model spans 155 RBP targets across three cell lines.
Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.
Cryo-electron tomography membrane analysis pipeline pairing generalizable U-Net membrane segmentation with mesh-based particle localization.
Ab initio cryo-EM structure modeling that labels density voxels by atom and amino acid type, then threads sequences through them with an HMM.
Codon-level BERT model that captures genomic signals invisible to amino acid models, outperforming billion-parameter PLMs with just 86M parameters.
Protein function prediction models that assign Gene Ontology terms using language model embeddings and neuro-symbolic reasoning over GO axioms.
Peptide language model that generates antimicrobial, anticancer, and target-binding sequences, adapted per task by Mixture-of-Experts plugins.
Inverse protein folding from backbone coordinates, chaining a pretrained structure encoder into a pretrained sequence autoencoder on small data.
Binding energy for protein-ligand, protein-protein, and antibody-antigen complexes is read off an energy model trained on crystal structures alone.
Controllable protein design by prefix-tuning a protein language model with learned virtual tokens that combine for multi-property generation.
Structure-aware antibody language model with sequence embeddings contrastively aligned to crystal structures, reaching AUROC 0.875 on HER2 binding.
Antibody language model pretrained on 779M human B cell receptor sequences, reading a heavy chain, a light chain, or both paired in one input.
Zero-shot variant effect prediction that fuses a frozen protein language model with an equivariant graph network over residue contact graphs.
AlphaFold fine-tuned via OpenFold on 944 high-resolution MHC-peptide structures, reaching median peptide RMSD of 0.65 Å on held-out complexes.
Protein structure tokenizer that encodes all-atom folds as artificial amino acids and decodes them back to coordinates at TM-score above 0.96.
Sequence-only cancer driver mutation predictor combining ProtT5-XL embeddings with per-position evolutionary statistics and a confidence score.