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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 392 filtered models
De novo ligand design framework that generates protein binders and small molecules by inverting gradients through a differentiable docking model.
Graph diffusion transformer for in-context molecular design, adapting to new tasks from a few molecule-property demonstrations without 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.
Discrete flow generative model over fragmented SMILES for de novo, fragment-constrained, and property-optimized small-molecule drug design.
Sequence-based protein-ligand binding site predictor pairing a protein language model with a SMILES chemical language model for zero-shot ligands.
Spectroscopy-grounded molecular foundation model that reads NMR, IR, and mass spectra as text, elucidating structures and generating 3D conformers.
Multimodal drug-target interaction model aligning SMILES, molecule text, taxonomy, and protein sequence with a Gramian volume contrastive objective.
Structure-free RNA-small molecule binder discovery model that predicts ligands and their binding sites from RNA sequence using an RNA language model.
Multimodal scientific foundation model unifying protein, DNA/RNA, and small-molecule structure in one token vocabulary for cross-domain reasoning.
SE(3)-equivariant chemical language model for pocket-based 3D molecule generation, used to design an HPK1 inhibitor with in vivo anti-tumor efficacy.
Chemical language model for fragment-based drug discovery, trained on all 62M ZINC-22 fragments. Samples 99.9% chemically valid fragment SMILES.
Protein language model that predicts which of eight lipid categories a protein binds from sequence alone, plus binding sites and mutation effects.
Discrete graph diffusion model for multi-property molecular generation, composing per-property score guidance over arbitrary condition subsets.
Protein-ligand binding site prediction that ranks pocket residues and pocket center coordinates, staying accurate on AlphaFold-predicted structures.
Sequence-based multitask model predicting covalently ligandable cysteines and reversible ligand-binding residues across the human proteome.
RNA-small molecule binding affinity prediction from RNA sequence and compound SMILES, pairing a 56M-parameter RNA language model with ChemBERTa-2.
RNA small-molecule binding site prediction from sequence alone, pairing frozen RiNALMo embeddings with a lightweight MLP classifier.
Binding free-energy surrogate trained on 1.4M molecular dynamics frames, ranking docking poses ~28,000x faster than physics-based MMGBSA.
De novo drug design model generating target-conditioned ligands by latent diffusion over 1D SELFIES strings, conditioned on protein sequence alone.
Protein-ligand interaction predictor that types seven contact classes between residues and ligand functional groups from sequence and SMILES alone.
Genomic language model predicting drug-induced translational readthrough at premature stop codons, with an R2 of 0.94 across eight compounds.
Structure-based drug design model generating 3D ligands in a protein pocket with interaction-guided flow matching and a learned atom-count predictor.