Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 392 filtered models
Residue-level prediction of ligand-binding and cryptic pockets in one model, reading frozen ESM-2 embeddings with no 3D structure at inference.
Molecular graph foundation model for ADMET and pharmacokinetic property prediction, pretrained by contrastive learning with graph external attention.
Single-cell perturbation prediction model trained only on synthetic priors, inferring drug targets, intervention strengths, and regulatory graphs.
Molecular glue degrader design model that jointly generates the glue molecule and the E3 ligase-target ternary complex from unbound monomers.
Optical chemical structure recognition model that turns molecule images into SMILES, reaching 93.8% exact match on USPTO with full stereochemistry.
Vision-language model that parses Markush structures from patents in a single stage, turning whole-image drawings into machine-readable CXSMILES.
Molecular ensemble foundation model for cyclic peptides, pooling per-conformer EGNN embeddings to predict membrane permeability from 3D structure.
Molecule generation conditioned on single-cell transcriptomes, designing cell-type-specific compounds that revert diseased cell states.
Protein-ligand binding affinity prediction from sequence and SMILES, without MSAs. Coarse-grained cofolding runs over 10x faster than Boltz-2.
Molecular representation learning from surface point clouds, 3D graphs, and fragment tokens. Cuts ESOL RMSE to 0.740 under scaffold splitting.
Generative language model that designs drug-like SMILES conditioned on disease ontology and a target protein sequence for de novo drug discovery.
Structure-based drug design language model fusing protein structural and evolutionary encoders with SAFE fragment tokens for hit-to-lead generation.
Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.
Vision-language model that reads molecular structure images, translating them to SMILES, captions, and properties via chemical-bond topology.
Diffusion model for 3D small-molecule design against protein-protein interaction sites, guided by the natural binding peptide or protein partner.
Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.
Molecular foundation model that turns SMILES into 2048-dimensional embeddings from multiple 3D conformations for ADMET and virtual screening.
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.
Zero-shot generative framework that turns 3D pharmacophores into synthesis-ready DNA-encoded libraries of purchasable building blocks.