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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 265–288 of 392 filtered models
Goal-oriented de novo molecule design with an instruction-tuned LLM that honors property targets, substructure constraints, and numeric values.
Ternary complex structure predictor for PROTACs and molecular glues, placing E3 ligase, degrader, and target protein in one SE(3)-equivariant pass.
Cell Painting image encoder that turns whole-slide multi-channel microscopy into morphological profiles in one pass, with no cell segmentation step.
Inverse molecular design conditioned on transcriptomics, generating small molecules intended to revert a diseased cell to a healthy expression state.
Protein-ligand affinity foundation model that embeds pockets and ligands in one space, unifying virtual screening with hit-to-lead optimization.
Broad-spectrum antiviral screening framework pairing a pretrained molecular encoder with ESM-2 embeddings for phenotype- and target-based prediction.
Instruction-tuned LLMs for multi-property molecule optimization, rewriting a hit compound to improve three or more drug properties at once.
3D molecule generation that writes a valid 1D SELFIES string with a pretrained language model, then predicts its conformer with a diffusion module.
Multimodal small-molecule foundation model contrastively pretrained over SMILES, molecular graphs, and fingerprints for antibiotic screening.
Enzyme kcat and KM prediction from sequence and substrate SMILES, binned by order of magnitude so catalytic-site mutations shift the prediction.
Unified science foundation model treating molecules, proteins, RNA, DNA, and materials as one sequence language, in 1B, 8B, and 46.7B sizes.
Kinase-inhibitor binding affinity prediction fusing a contrastively pretrained molecular graph encoder with structure-informed kinase embeddings.
Structure-based drug discovery transformer that handles protein-ligand docking and pocket-aware 3D molecule design in one pretrained model.
Perturbation representation model embedding CRISPR gene targets and small molecules in one space, transferring genetic screen models to drug response.
Autoregressive graph generator that flattens molecules into token sequences, letting a decoder-only transformer sample valid structures in one pass.
Attention architecture fusing docking scores with protein language model embeddings, so an enzyme gets a different representation per substrate.
SMILES generative model for de novo drug design, pretrained on 200 million ZINC20 compounds with a tokenizer built from frequent substructures.
Structure-based drug design model generating 3D ligands inside a protein pocket, aligned by Best-of-K fine-tuning on drug-likeness and docking.
Molecular docking framework that poses several ligands sharing one protein pocket at once, using their consistency to sharpen each prediction.
Adenylation domain substrate specificity prediction from frozen ESM-2 embeddings, with zero-shot calls on substrates absent from training.
Binder motif prediction from receptor structure alone, mapping 14 functional-group types across a protein surface as reusable interaction profiles.
Drug-target interaction model that compresses any compound into a 15-bit hierarchical code, so billion-compound libraries can be screened in seconds.