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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 289–312 of 392 filtered models
Autoregressive graph transformer generating molecules as node and edge token sequences, fine-tunable for goal-directed design and property prediction.
Cell Painting generative model encoding lab, batch, and well position as causal variables, predicting mechanism and target for unseen compounds.
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.
Latent diffusion model that paints high-resolution Cell Painting images of cells responding to a chemical compound or an over-expressed gene.
Retention time prediction for peptides whose post-translational modifications were never seen during training, using molecular-structure encodings.
Protein-ligand binding affinity prediction from multimodal representations. Retains accuracy on predicted rather than crystal complex structures.
Geometric foundation model matching enzymes to the reactions they catalyze, trained on 1.5 million structure-informed enzyme-reaction pairs.
Latent diffusion model generating Cell Painting images for a compound from its predicted bioactivity profile, reaching unfamiliar chemical matter.
PROTAC degrader generation pipeline that screens target-binding fragments, then builds molecules under structure and physicochemical constraints.
Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.
Ligand identification in cryoEM and X-ray density maps, classifying a density blob into one of 219 ligand groups from its 3D point cloud shape.
SMILES language model pretrained by editing: substructures are dropped and restored, giving fragment-level supervision for property prediction.
Pocket-conditioned 3D ligand generator built on rectified flow, reaching -8.50 average Vina Dock and 75.0% diversity on CrossDocked2020.
Drug-target affinity prediction pairing an SE(3)-equivariant GNN over 3D protein structure with a molecular GNN and residue-atom cross-attention.
Molecular formula identification from tandem mass spectra at 88.3% top-1 accuracy, more than 10x faster than fragmentation-tree search.
Flexible protein-ligand docking that repacks the pocket side chains from the backbone and the ligand graph before a physics sampler places the ligand.
Vector-based virtual screening model that co-embeds proteins and small molecules so a drug-target interaction reduces to a single dot product.
De novo peptide design across non-canonical amino acid space, using guided diffusion over receptor-ligand interfaces to reach D-amino acid chemistry.
Metabolite annotation model embedding MS/MS spectra and molecular graphs in one contrastive space, ranking candidate structures by cosine similarity.
Target-conditioned generative language model that designs drug-like SMILES from a protein sequence, tuned on regulator-approved drug-target pairs.
Structure-based molecule optimization that steers a Bayesian flow network with property gradients over 3D coordinates and atom types at once.