Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 25–48 of 262 models
Autoregressive DNA foundation model for variant effect prediction, using 6-mer tokenization to match Evo2-7B win rates at far higher throughput.
Reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal LLM understanding expert with a Boltz-1 diffusion expert.
110M-parameter RNA language model that designs sequences from secondary structure, motif, and Gene Ontology constraints via discrete diffusion.
Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.
Chemical language models pretrained on SMILES for therapeutic peptides, natively representing non-canonical residues, cyclization, and conjugation.
Molecular linker design model fine-tuned from Llama 3 that emits PROTAC and fragment linkers as SMILES from natural-language geometry prompts.
Generative DNA foundation model trained on 91.7M nucleotide sequences and annotations for species classification and mutation effect prediction.
Multimodal diffusion model that co-designs protein sequence and 3D structure around cofactors and small molecules for de novo heme enzyme design.
Whole-cell segmentation model for spatial transcriptomics that fuses DAPI nuclear images with RNA transcript density to recover true cell boundaries.
Transformer that classifies tumour types and subtypes from somatic variants in whole-genome and whole-exome data, with auto-downloading checkpoints.
Long-context plant DNA language model, 676M parameters on a Mamba2 backbone, pretrained on 65 angiosperm genomes for cross-species variant annotation.
Autoregressive model for therapeutic mRNA design that jointly generates 5' UTR, CDS, and 3' UTR, pretrained on 30 million full-length natural mRNAs.
Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.
Generative RNA foundation model trained on 114 million full-length sequences for de novo design of tRNAs, aptamers, CRISPR guide RNAs, and mRNAs.
Molecular foundation models pretrained on density functional theory data, encoding 3D geometry and quantum behavior for ADMET and drug discovery.
Histopathology foundation model with 1.1B parameters, trained entirely on public data using JEDI, a dual-stage strategy combining JEPA and DINO.
Diffusion language model with 4.9 billion parameters that predicts genome-wide CRISPRi perturbation responses in single-cell transcriptomes.
Predicts protein complex stoichiometry from amino acid sequence alone, ranking copy numbers in seconds and exporting AlphaFold3-ready JSON files.
Flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, including carbohydrate binders.
Coding-sequence foundation model for mRNA design, pretrained as a BART denoising encoder-decoder on mRNA from nine taxonomic groups.
Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.
Post-hoc method that restores monotonic scaling to ESM-2 embeddings, yielding Matryoshka-style nested representations for variant effect prediction.
Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cell trajectories over time.
Small-molecule drug discovery foundation model covering ADMET, retrosynthesis, drug-target activity, and molecular optimization in a 2.6B checkpoint.