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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 241–264 of 1004 filtered models
Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.
Antibody developability predictor pairing text and protein language models, using in-context learning to fit new assays without retraining.
Multimodal architecture coupling pretrained DNA, RNA, and protein language models with directional cross-attention into one Virtual Cell Embedding.
Peptide developability predictor scoring solubility, permeability, toxicity, and binding from amino-acid sequences or chemically modified SMILES.
Ab initio heterogeneous cryo-EM reconstruction seeds its encoder with foundation-model priors, sorting 100 structures from one simulated mixture.
All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.
Protein-family language model trained on unaligned homolog sets for zero-shot variant fitness prediction and design. ProFam-1 holds 251M parameters.
De novo protein binder design suite from ByteDance pairing diffusion and hallucination generators with confidence-based filtering of designs.
Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.
De novo peptide sequencing transformer that reads modified and unmodified peptides directly from tandem mass spectra without a reference database.
Energy-based model of protein conformational space, turning a diffusion model into a statistical potential for structure ranking and mutation scoring.
Structure-based drug design model that unifies de novo generation, docking, conformer generation, and pharmacophore conditioning via flow matching.
Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.
Structure-conditioned protein sequence design, pairing a three-track architecture with discrete flow matching for fast, few-step inverse folding.
Retrieval-augmented diffusion model for protein inverse folding that conditions sequence generation on profiles from structurally similar homologs.
Mutation effect prediction at protein–DNA and protein–RNA interfaces, combining frozen ESM-2 embeddings with an edge-aware atomic graph network.
Protein sequence encoder that maps ESM2 embeddings to a learned 20-letter alphabet for structure-quality remote homology detection at MMseqs2 speed.
RNA interaction foundation model for conditional, zero-shot design of RNA sequences that bind protein, DNA, or RNA targets without retraining.
Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.
Multimodal protein representation model that iteratively fuses a sequence language model with a 3D structure encoder through a shared learnable token.
Flow-matching model that jointly samples 3D de novo molecules and several low-energy conformers, extending to pocket-conditioned ligand design.
Diffusion model for structure-based drug design that jointly generates 3D ligands and holo pocket conformations from an apo protein structure.