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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 193–216 of 392 filtered models
Structure-based drug design model that inpaints a 3D ligand density into an empty protein pocket, then decodes those voxels into valid SMILES.
Structure-based drug design model that generates 3D ligands inside a protein pocket by interpolating distribution parameters instead of samples.
Synthesizable 3D molecule generation that jointly samples building blocks, reactions, and atomic coordinates, returning a synthesis route per design.
Scoring function for protein, nucleic acid, and small-molecule complexes that predicts binding affinity, ranks docked poses, and screens ligands.
Conditional chemical language model prompted with a protein target and mechanism of action to score and design molecules without structural input.
Antimicrobial discovery model predicting compound potency against unseen bacterial strains and generating de novo antibiotics from pathogen genomes.
Site-specific structure prediction conditioning AlphaFold3 diffusion on a fixed receptor and known binding pocket. 81.2% success on PoseBusters V2.
Structure-based 3D molecule generation with one diffusion backbone for fragment growing, linker design, scaffold hopping, and side-chain decoration.
Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.
All-atom biomolecular structure prediction with adapters for allosteric states, user-defined interface constraints, and binding affinity.
Molecular conformer generation from 2D graphs with a diffusion transformer that replaces equivariant layers with graph shortest-path attention biases.
Structure-free ligand generation conditioned on a protein sequence alone, using masked diffusion over SMILES trained on 1.2M BindingDB active pairs.
Tandem mass spectrometry model that embeds MS/MS spectra and molecular graphs in one space, ranking candidate structures without a spectral library.
Multimodal drug-response model coupling cell and molecule foundation models, pretrained on 1.8M perturbation RNA-seq profiles over 22,000 compounds.
Structure-based drug design model pairing SE(3)-equivariant diffusion with retrieval of pocket-matched scaffolds to generate ligands for a target.
Graph-level self-supervised pretraining for 3D molecules, reconstructing whole-molecule geometry to sharpen quantum property and force prediction.
Ligand-binding protein design driven by a natural-language function description plus a ligand SMILES string, in 1B and 3B parameter variants.
Generative chemistry foundation model pairing a text-and-SMILES language backbone with a 3D molecular point-cloud encoder for prediction and design.
Virtual drug screening from per-atom protein and ligand embeddings retrieved by nearest neighbors. 30.4 EF1% on DUD-E at ~14 s per million molecules.
Structure-based virtual screening model that scores ligands against apo and predicted pockets, lifting blind-apo EF1% on DUD-E from 11.75 to 37.19.
Relative protein-ligand binding affinity prediction from docked complexes, matching Schrodinger FEP+ ranking accuracy zero-shot on the FEP benchmark.
Cross-modal co-embedding of biosynthetic gene clusters and natural products, enabling bidirectional retrieval between gene cluster and compound.
De novo small-molecule generation inside a protein pocket, diffusing ligand atoms and pocket side chains together so the site adapts as it binds.