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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 385–408 of 2565 models
Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.
Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.
De novo nanobody design generating epitope-targeted VHH binders from a target sequence, with only 14-50 candidates sent for experimental testing.
Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.
Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.
All-atom antibody-antigen complex structure prediction on an AlphaFold 3-inspired architecture, served as a closed model through MoleculeOS.
Graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero-shot disorder classification.
Generative model for chemically modified and macrocyclic peptides that builds molecules in HELM notation, supporting de novo design and infilling.
Motion artifact reduction for cardiac CT, folding a learned cardiac motion field into model-based reconstruction to image the heart at any phase.
Protein representation learning from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.
Generative microscopy foundation model that synthesizes in-silico fluorescence images of protein subcellular localization from amino-acid sequence.
Self-supervised foundation model that embeds cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types for tumor subtyping.
TCR-epitope binding prediction and tumor-reactive T-cell identification in one heterogeneous graph transformer, reaching AUROC 0.937 on IEDB.
Masked discrete-diffusion model over millions of full-length mRNAs, steered by Monte Carlo tree search for joint codon optimization and UTR design.
Transcriptomics foundation model from Recursion that masks and reconstructs RNA-seq gene expression counts to learn reusable sample embeddings.
Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.
Tri-modal foundation model unifying histology images, spatial transcriptomics, and language for zero-shot pathology and spatial biology reasoning.
Self-supervised foundation model for continuous glucose monitoring, with dual streams separating slow physiological state from transient events.
Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.
Physics-informed generative foundation model for quantitative diffusion MRI that maps brain microstructure and adapts zero-shot to each participant.
Diffusion transformer for virtual tissue synthesis, generating H&E histopathology patches conditioned on spatial gene expression and morphology.
Genetically aligned foundation model for blood smear cytology that links single-cell morphology to the chromosomal aberrations behind AML and APL.