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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 457–480 of 2336 models
Molecular reasoning model built on DeepSeek-7B, using chain-of-thought and reinforcement learning for property prediction, generation, and reactions.
Coding-sequence foundation model for mRNA design, pretrained as a BART denoising encoder-decoder on mRNA from nine taxonomic groups.
Vision transformer trained with DINO self-supervision to segment micronuclei in DNA-stained fluorescence images across cell lines and microscopes.
DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match top genomic benchmarks.
Cell-centric microscopy foundation model that distills morphology and microenvironment views into a unified embedding for virtual spatial omics.
Phosphopeptide detectability prediction for mass spectrometry, rescoring DDA identifications and pruning DIA spectral libraries to cut search time.
Hierarchical language model for atlas-level cell-type annotation of scATAC-seq data that annotates new query datasets without retraining.
Paired-sequence protein language model that jointly encodes two interacting chains to predict interactions, binding affinity, and interface contacts.
Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.
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.
EEG-to-text foundation model that turns raw recordings into clinically grounded natural-language narratives instead of fixed-label classifications.
Bacterial proteome foundation model that learns contextualized gene and whole-genome representations from tens of thousands of complete genomes.
Protein inverse folding model aligning ProteinMPNN by multi-objective preference optimization to improve developability without losing fold fidelity.
Neural Hamiltonian flow for protein sequence generation with inference-time control over composition and net charge via analytical bias potentials.
Self-supervised transformer for population genetics, pretrained on 1000 Genomes data, that detects positive selection via haplotype-wise attention.
Cross-modal protein encoder that aligns ESM-2 sequence embeddings with ProteinMPNN structure embeddings in a shared space for cross-modal retrieval.
Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cell trajectories over time.
Renal pathology foundation model whose learning unit is a whole detected glomerulus, pretrained on over a million of them across four biopsy stains.
Renal pathology foundation model self-supervised on a million kidney biopsy tiles spanning glomeruli, interstitium, and surrounding structures.
Small-molecule drug discovery foundation model covering ADMET, retrosynthesis, drug-target activity, and molecular optimization in a 2.6B checkpoint.
DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.
Self-supervised SE(3) geometric pretraining for protein backbone generators, improving designability, motif scaffolding, and conformational ensembles.