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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 241–264 of 2336 models
Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.
Toxicity screening foundation model that encodes Tox21 concentration-response curves and assay metadata into reusable 768-dimensional embeddings.
Generative scientific foundation model that writes proteins, ligands and their binding interfaces as tokens in one shared grammar, at 1B to 8B scale.
Genomic language model from Radical Numerics with a 2 Mbp context window, built for zero-shot variant effect prediction and sequence design.
Generative transformer for phylogenetic inference that transduces sets of unaligned molecular sequences directly into Newick-format trees.
Conditional denoising diffusion model that designs antigen-specific TCR CDR3β sequences conditioned on peptide-MHC targets and germline V-genes.
Diffusion-based generative RNA model for de novo sequence design, conditioned on function, RNA family, structure, or binding proteins.
Protein binder generator producing receptor-conditioned binders from sequence alone, using a sparse Mixture-of-Experts transformer with no 3D input.
Reinforcement-learning generative framework for multi-objective RNA codon optimization that generalizes across six species and five RNA types.
Family of ten compact GPT-2 decoder-only DNA language models spanning BPE vocabularies from 16 to 8192 tokens, built for lossless genome compression.
Histopathology model reconstructing tissue-wide single-cell gene expression from H&E slides, using sparse TMA measurements as molecular anchors.
Metabolite annotation from tandem mass spectra by cross-modal retrieval, with a Tanimoto term keeping chemical neighbours close in the shared space.
Hallucination framework for de novo nucleic acid design, pairing NA-MPNN sequence proposals with a frozen AlphaFold3 or Protenix structure oracle.
Bulk RNA-seq foundation model learning normalization-robust transcriptome representations via TF-IDF gene ordering and masked gene modeling.
Reinforcement learning framework that fine-tunes the ProGen2-OAS antibody language model with GRPO to cut germline bias in generated sequences.
Message-passing neural network that designs buried hydrogen-bond networks onto protein backbones, combining learned placement with PyRosetta scoring.
860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.
Histopathology model predicting 102 methylation-defined CNS tumor subtypes from H&E whole-slide images, with calibrated per-case confidence scores.
RNA foundation model for m6A epitranscriptomics, pretrained on MeRIP-seq peak sequences to call base-resolution sites, regulator binding, and decay.
Multi-task antibody developability model predicting 18 biophysical endpoints from heavy- and light-chain sequence, trained on Lilly assay data.
Small-molecule ADME/Tox prediction from a SMILES string, covering 33 correlated ADMET endpoints in one multi-task model with conformal uncertainty.
Post-translational modification prediction for 12 PTM types in a single model, stratifying imbalanced training data with contrastive learning.
Reasoning LLM that predicts antimicrobial susceptibility of clinical bacterial isolates and supplies mechanistic explanations for each prediction.