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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 265–288 of 2336 models
Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.
Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.
Fine-tuned Enformer derivative that annotates cis-regulatory elements from DNA sequence, emitting enhancer, promoter, and insulator class labels.
Generative protein-dynamics model that predicts short molecular dynamics trajectories with rectified flow matching over residue frames and torsions.
Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.
Pathology vision foundation model adapting DINOv3 self-supervised learning to whole-slide histopathology across many magnifications and scales.
Variational autoencoder trained on scRNA-seq and applied frozen to impute unmeasured genes and denoise spatial transcriptomics profiles.
Enzyme-substrate specificity model that scores catalytic pairs from sequence with a physics-derived dual-encoder and a contrastive objective.
Specificity foundation model predicting small-molecule drug-target binding from sequence, scored as cross-modal retrieval without docking or assays.
CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.
Transformer that predicts protein-protein interactions at residue resolution, spanning mutations, PTMs, peptide-MHC binding, and disease variants.
Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.
Generative foundation model for antibody and multispecific design, doubling its predecessor's experimental success rate on therapeutic targets.
Peptide-MHC binding specificity model that frames presentation as cross-modal retrieval, aligning peptide and MHC encoders by contrastive learning.
Foundation model that predicts microRNA-mRNA target specificity from sequence, using a dual-encoder trained with a symmetric contrastive objective.
Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.
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.
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.
De novo nanobody design generating epitope-targeted VHH binders from a target sequence, with only 14-50 candidates sent for experimental testing.
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.