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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 433–456 of 2336 models
Contrastive dual-encoder model embedding protein domains and peptides in one space to predict domain-peptide binding specificity at proteome scale.
Generative adversarial network trained on single-cell and bulk RNA-seq for sample stratification, marker analysis, and synthetic data generation.
Multimodal molecular foundation model fusing SELFIES, 2D graphs, text, and knowledge graphs via contrastive pretraining for property prediction.
Structure-aware protein language model using structure-guided masking and a causal objective for variant effect prediction and protein discovery.
All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.
Multimodal reverse-translation language model that generates species-aware mRNA coding sequences from protein sequences, conditioned on host taxonomy.
Voice biomarker model that flags current type 2 diabetes from one 20-second speech recording, validated against HbA1c blood tests in 801 adults.
Protein language model that classifies RNA-binding proteins, localizes RNA-binding domains, and scores mutation effects at single-residue resolution.
Single-nucleotide-resolution RNA foundation model pretrained on non-coding RNAs with ELECTRA-style replaced-token detection for regulatory inference.
Virtual cell foundation model predicting single-cell responses to genetic, chemical, and cytokine perturbations with conditional flow matching.
Protein backbone design model pairing flow matching with a Mamba state-space backbone, generating long proteins in linear time with exact geometry.
Diffusion language model with 4.9 billion parameters that predicts genome-wide CRISPRi perturbation responses in single-cell transcriptomes.
Dual-encoder contrastive model that retrieves enzymes for query reactions by matching reaction fingerprints to protein sequence embeddings.
Multimodal foundation model integrating spatial transcriptomics, H&E histopathology, and pathway scores for single-cell niche discovery.
Predicts protein complex stoichiometry from amino acid sequence alone, ranking copy numbers in seconds and exporting AlphaFold3-ready JSON files.
Geometric deep learning model that learns atomic-scale representations of molecular interfaces across proteins, small molecules, and nucleic acids.
Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.
Brain MRI synthesis in 3D across T1w, T2w, FLAIR and SWI, whole-brain or skull-stripped, at up to 512x512x256 voxels in 30 sampling steps.
Flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, including carbohydrate binders.
Sequence-to-ensemble predictor that generates conformational ensembles of intrinsically disordered proteins zero-shot, with no per-sequence refitting.
All-atom generative foundation model that designs small molecules, peptides, and nanobodies against a target binding site from a single checkpoint.
Generative virtual-cell model predicting whole-transcriptome responses to unseen compounds and genetic perturbations, from cell lines to organoids.
Virtual staining model that generates four IHC markers, HER2, Ki67, ER, and PR, from H&E using a generator conditioned on a frozen UNI encoder.