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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 289–312 of 1004 filtered models
Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.
Protein structure autoencoder compressing backbone coordinates into a latent space, paired with a latent diffusion model for generative design.
Autoregressive protein language model for antibody Fc domains, reinforcement-tuned to design variants with programmable Fc-receptor binding profiles.
De novo ligand design framework that generates protein binders and small molecules by inverting gradients through a differentiable docking model.
Protein foundation model with 3B parameters, pretrained jointly on sequence and 3D structure via masked language modeling and diffusion denoising.
Compact protein fitness predictor that fuses within-family evolutionary profiles with inverse-folding logits for zero-shot variant effect prediction.
All-atom protein representation model that learns from each residue's strictly local atomic neighborhood, capturing side-chain geometry and chemistry.
Protein language model conditioned on ensembles of computed conformations, giving state-aware embeddings for interaction, localization, and function.
Nucleic acid inverse-folding network that designs RNA sequences for a target 3D backbone and predicts protein-DNA binding specificity.
GPCR structure prediction and peptide design model that generates linear and cyclic peptide agonists carrying noncanonical amino acids, zero-shot.
Distilled few-step protein backbone generator that adapts Score Identity Distillation to Proteina for over 20x faster de novo structure sampling.
De novo peptide sequencing model that aligns tandem mass spectra with protein language model embeddings through constrained optimization.
Structure-conditioned fine-tune of ESM2 for protein mutation-effect prediction, matching ESM3-level accuracy after roughly an hour of fine-tuning.
SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.
Geometric deep learning scoring function for protein-ligand binding affinity, pretrained on synthetic complexes and fine-tuned on PDBbind structures.
Multimodal biomedical framework aligning frozen single-cell and protein model encoders to an LLM's embedding space for zero-shot reasoning.
Protein structure tokenizer that maps 3D backbones to discrete tokens with an SE(3)-equivariant encoder preserving orientation and chirality.
Potts-model inverse folding that conditions on a structural ensemble rather than a single backbone, improving designability and self-consistency.
Sequence-based protein-ligand binding site predictor pairing a protein language model with a SMILES chemical language model for zero-shot ligands.
Protein structure prediction model that weights residues by protein-language-model importance scores to improve accuracy on hard AlphaFold2 targets.
Protein-protein binding affinity prediction from sequence alone, pairing frozen protein language model embeddings with gradient-boosted trees.