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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 97–120 of 1004 filtered models
Multimodal molecular generation model for drug design, conditioned on properties, pharmacophores, protein sequences, or protein binding pockets.
Diffusion model that generates 3D small molecules conditioned on protein pockets and partial fragments encoded as continuous spatial density maps.
Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.
De novo protein binder and nanobody design pipeline that ranks candidates by a protein-protein interaction model rather than structural confidence.
Small-molecule hit-discovery pipeline using Boltz-2 co-folding and affinity prediction to rank in-stock compounds or make-on-demand chemical space.
Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.
Generative scientific foundation model that writes proteins, ligands and their binding interfaces as tokens in one shared grammar, at 1B to 8B scale.
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.
Protein binder generator producing receptor-conditioned binders from sequence alone, using a sparse Mixture-of-Experts transformer with no 3D input.
Hallucination framework for de novo nucleic acid design, pairing NA-MPNN sequence proposals with a frozen AlphaFold3 or Protenix structure oracle.
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.
Multi-task antibody developability model predicting 18 biophysical endpoints from heavy- and light-chain sequence, trained on Lilly assay data.
Post-translational modification prediction for 12 PTM types in a single model, stratifying imbalanced training data with contrastive learning.
Generative protein-dynamics model that predicts short molecular dynamics trajectories with rectified flow matching over residue frames and torsions.
Enzyme-substrate specificity model that scores catalytic pairs from sequence with a physics-derived dual-encoder and a contrastive objective.
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.
Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.
De novo nanobody design generating epitope-targeted VHH binders from a target sequence, with only 14-50 candidates sent for experimental testing.