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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Structure-based molecular generation guided by imputed ligand electron density, assembling drug-like compounds into a pocket fragment by fragment.
De novo 3D drug design that turns a pharmacophore arrangement into a molecule through an SE(3)-equivariant diffusion bridge.
Single-cell epigenomic foundation model that reads scATAC-seq as cell sentences of accessible cCREs, pretrained on about 5 million human cells.
Latent diffusion model that paints high-resolution Cell Painting images of cells responding to a chemical compound or an over-expressed gene.
Protein language model that generates paired heavy and light chain human antibodies from an antigen prompt, with binders validated in vitro.
Protein and RNA sequence annotation that also names the residues driving each label, learned from sequence-level supervision alone.
Hi-C foundation model pretrained on 118 million contact submatrices, fine-tuned for loop detection, resolution enhancement and epigenomic prediction.
Transcription factor binding-site prediction from DNA sequence, recast as 23-way DNA-binding-domain classification with a fine-tuned DNABERT.
RNA secondary structure and 3D motif prediction from alignments, using a probabilistic grammar that places over 50 known motifs by covariation.
Retention time prediction for peptides whose post-translational modifications were never seen during training, using molecular-structure encodings.
Resolution enhancement for sparse single-cell Hi-C contact matrices, using a cascading residual GAN with self-attention over chromatin loci.
Protein-ligand binding affinity prediction from multimodal representations. Retains accuracy on predicted rather than crystal complex structures.
Contact map and interface residue prediction for intrinsically disordered regions from sequence, outperforming AlphaFold-Multimer and AlphaFold3.
Single-cell RNA-seq representation model that separates batch-dependent from batch-independent variation to compare disease states across datasets.
Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.
Cell type annotation from multiplexed tissue images, using a pretrained Vision Transformer ensemble that runs on new panels without fine-tuning.
Histopathology vision-language foundation model that folds a disease knowledge graph into pretraining for zero-shot cancer detection and subtyping.
Histopathology vision-language model handling image patches and gigapixel slides in one 15B checkpoint, across classification, VQA, and captioning.
DNA- and RNA-binding residue prediction from a nucleic-acid-adapted protein language model and an equivariant graph network over protein structure.
Geometric foundation model matching enzymes to the reactions they catalyze, trained on 1.5 million structure-informed enzyme-reaction pairs.