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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 217–240 of 392 filtered models
Instruction-tuned LLM series for multi-property molecule optimization that improves named drug properties while preserving those already in range.
Protein-ligand docking model for high-throughput virtual screening, predicting binding poses with graph neural networks at low computational cost.
Molecular embedding model that turns SMILES into SE(3)-invariant vectors for property prediction, similarity search, and compound clustering.
Cyclic peptide design conditioned on target protein structure, generating all four cyclization types via all-atom, all-bond harmonic SDE modeling.
Protein-ligand binding affinity model that tokenizes quantum electron-cloud density into discrete codes, plus a distilled cloud-free variant.
Structure-constrained molecular generation using reinforcement learning over reaction templates, trained without any external property metric.
Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.
Molecular dynamics emulator generating time-coarsened trajectories for small molecules, peptides, and proteins from one shared atomic representation.
Structure-based drug design by diffusing medicinal-chemistry fragments into a binding pocket, yielding synthesizable, selective, drug-like molecules.
Pharmacophore-conditioned diffusion model generating 3D molecular graphs that satisfy a given pharmacophore hypothesis without a target structure.
Ligand-aware protein language model that cross-attends SaProt embeddings to ligand SMILES, beating SaProt across six downstream benchmarks.
Molecular conformation description language encoding 3D geometry as SMILES plus internal-coordinate tokens, turning 3D modeling into a sequence task.
Structure-based drug design that schedules noise separately for 3D coordinates and 2D topology, reaching a 95.9% PoseBusters valid rate on CrossDock.
Graph neural network that reconstructs chemical bonds and bond orders from 3D coordinates, distorted generated geometries, or bare 2D connectivity.
Pocket-conditioned 3D diffusion model for scaffold decoration, guided by evolutionary residue conservation and a protein-ligand interaction prior.
Retrosynthesis and reaction prediction LLM that jointly learns molecular fragmentation and recombination from a 4.4M-instruction chemistry corpus.
Generative diffusion model that samples biomolecular conformational ensembles for proteins, RNA, and ligands in hours instead of millisecond-scale MD.
Physics-guided all-atom diffusion model for protein-ligand complex prediction, reaching 95.3% success on PoseBusters redocking with a known pocket.
Cryptic protein binding site prediction from sequence, backed by a database of 5,151 cryptic sites mined from 6 million apo-holo PDB alignments.
TCR-peptide binding prediction that fuses ESM-1b receptor embeddings with MolFormer SMILES embeddings through multi-head cross-attention.
Pocket-based molecular docking with an SE(3)-equivariant diffusion transformer. Places 77.65% of top-1 poses within 2 Å RMSD on PoseBusters.
Predicts protein-ligand complex structures and binding affinity from sequence and ligand SMILES, without a PDB structure or a predefined pocket.
Flexible protein-ligand docking and binding affinity prediction from an apo receptor structure and ligand SMILES, using an 8-layer pair transformer.