Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 361–384 of 1004 filtered models
Predicts residue-residue dynamic contact maps from a single sequence, matching molecular dynamics ensemble methods at a fraction of the compute.
Simplex diffusion model for discrete sequence generation, with released checkpoints for DNA enhancer design and de novo protein sequence design.
Protein-ligand binding model that embeds ligands, pockets, and sequences in hyperbolic space, unifying virtual screening and affinity ranking.
All-atom protein structure diffusion models for motif scaffolding and hotspot-conditioned complex generation, at 22M parameters.
Protein function prediction via compressed in-context learning on a sequence-structure language model, cutting 751-token demonstrations to under 16.
Multimodal foundation model predicting genome-wide binding of chromatin-associated proteins from protein sequence, DNA sequence, and chromatin state.
B-cell epitope predictor pairing CNN and Transformer branches over protein language model embeddings to score linear and conformational epitopes.
Ice-binding protein classifiers over frozen ESM-2 embeddings, separating antifreeze from ice-nucleation proteins across bacterial proteomes.
Protein stability predictor scoring ΔΔG for point mutations by fusing ESM-2 embeddings with ProteinMPNN backbone geometry. Wet-lab validated.
De novo transmembrane protein design by joint all-heavy-atom sequence and structure diffusion, validated by a 1.7 A crystal structure.
All-atom structure prediction for arbitrary biomolecular complexes of proteins, nucleic acids, and ligands, with code and weights under a BSD license.
Structure-based drug design pipeline generating 3D molecules in binding pockets, raising zero-shot CrossDocked2020 docking success from 53% to 64%.
Structure-based virtual screening that rescores docking poses with a deep learning model, reaching 2.6x the enrichment factor of AutoDock Vina.
Cas9 PAM preference prediction from protein sequence with an ESM-2 backbone, extending PAM annotation to 50,308 metagenome-mined orthologs.
Blind protein-ligand docking model adding Ollivier-Ricci curvature descriptors and degree-aware message passing, predicting poses in 0.09 seconds.
De novo protein design model that co-generates sidechains, backbone, and sequence in one flow-matching process instead of backbone only.
Tri-modal contrastive model aligning protein structure, sequence, and text in a shared space for zero-shot cross-modal retrieval and classification.
RNA language model transfer-trained from ESM-2 via a pseudo-protein alphabet mapping, outperforming 12 RNA language models in zero-shot evaluation.
Model quality assessment network predicting per-residue lDDT, CAD-score, and RMSD for protein loops in predicted and designed structures.
Gram-negative bacterial effector prediction refining frozen ESM-1b embeddings with a mixture of convolutional experts and a transformer.
Blind flexible protein-ligand docking model trained by two-player self-play, predicting bound ligand and pocket poses in 0.32 seconds per complex.
Generative diffusion transformer for protein-ligand dynamics that produces trajectories, inpaints missing ligand atoms, and samples transition paths.
GPCR ligand bioactivity predictor combining ProteinBERT receptor embeddings with molecular descriptors, spanning the class A receptor family.
Amyloidogenicity predictor that classifies hexapeptides and scans whole proteins for aggregation-prone regions using frozen ESM-2 embeddings.