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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 793–816 of 1004 filtered models
Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.
Pocket-conditioned peptide designer: twin diffusion models generate an inhibitor backbone from receptor pocket geometry, then predict its sequence.
De novo peptide generation from four ProtGPT2 fine-tunes, one per design goal: hemolytic, non-hemolytic, non-fouling, and soluble sequences.
All-atom structure tokenizer that turns proteins, RNA and small molecules into discrete 3D tokens and decodes them back below 1 Å RMSE.
Protein complex interface quality assessment model that pairs persistent homology barcodes with a graph attention network to predict DockQ scores.
De novo peptide sequencing model that retrieves a similar peptide-spectrum match from a database and fuses it into transformer decoding.
Refines the CDR loops of a predicted antibody structure with SE(3) flow matching, steered at sampling time by bond, angle and torsion potentials.
Protein inverse folding by categorical diffusion, tuned with reinforcement learning on structural consistency. Samples diverse refoldable sequences.
Molecular docking and design foundation model that unifies structure-based drug design and peptide design at the atom level in one checkpoint.
Distinguishes experimentally resolved protein structures from predicted ones, pairing a Foldseek 3Di structural language model with a GVP-GNN.
Multimodal protein function annotation that scores a sequence against free-text descriptions, including GO and EC labels unseen during training.
Light-weight rigid protein-ligand docking model adapting the AlphaFold2 architecture, with a binding affinity module for virtual screening.
Retrieval-augmented diffusion model that designs antibody CDR sequences by conditioning on structurally homologous CDR-like motifs from the PDB.
Walk-jump sampler that runs molecular dynamics in a smoothed, noised space of all-atom coordinates to generate peptide conformational ensembles.
Multimodal discrete-diffusion protein language model that co-generates amino acid sequence and 3D backbone structure from a single transformer.
Protein domain annotation model pairing an ESM-2 backbone with a probabilistic decoder, bringing language-model sensitivity to Pfam-style assignment.
Context-only BERT for bacterial protein function prediction, reading genomes as sentences of protein-cluster tokens with no sequence input.
Blind protein-ligand docking as a single transformer pass over distance matrices, at hundredths of a second per complex on one GPU.
Fine-tuned Prosit predictor of spectra and retention time for citrullinated peptides, separating them from isobaric deamidation in MS searches.
Protein-ligand docking framework that picks the binding pocket by contrastive alignment, then refines the pose with bi-level iterative refinement.
Mixed-modal DNA, RNA, and protein foundation model at 110M and 270M parameters, with in-context learning across sequence modalities.
Per-residue classifiers fine-tuned from ESM-2 and ProtT5 that label 20 UniProt protein features and read out what a missense variant disrupts.