All Competitors

Diffusion-based structure prediction model for biomolecular complexes, spanning proteins with DNA, RNA, small molecules, ions, and modified residues.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

Missense variant effect predictor that distills GEMME evolutionary scores into a small head over frozen protein language model embeddings.

AI-designed CRISPR-Cas9 gene editor generated by protein language models trained on 1.2 million CRISPR operons and shown to edit the human genome.

Structure-based drug design that generates 3D ligands for a protein pocket entirely in the continuous parameter space of a Bayesian flow network.

Protein language model that predicts which of twelve subcellular compartments and biomolecular condensates a human protein partitions into.

Protein language model pairing sequence with quantized local-structure tokens via disentangled attention, for zero-shot variant effect prediction.

Structure-based drug design that steers a frozen 3D molecule diffusion model with user-supplied pharmacophore points inside a protein pocket.

Protein language model-based sequence search that detects remote homologs with threefold higher sensitivity than MMseqs2 at comparable speed.

Equivariant graph network that embeds a protein domain structure as a 128-dimensional vector, turning fold recognition into nearest-neighbour search.

Protein language models pretrained on Rosetta biophysics simulations rather than evolutionary data, then finetuned on small experimental assays.

De novo peptide sequencing from tandem mass spectra, re-weighting training losses by conditional mutual information to recover modified residues.

Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.

Antimicrobial peptide generator running denoising diffusion in the continuous ESM-2 embedding space, validated in mouse infection models.

Target-aware peptide design by flow matching on torus manifolds, generating a peptide's torsion angles, pose and sequence inside a receptor pocket.

Discrete-diffusion protein language model pretrained on UniRef50 that generates sequences and gives embeddings that outperform ESM-2 on prediction.

Blind protein-ligand docking that transfers to binding domains absent from training, scoring 22.6% top-1 on DockGen and 50% on PoseBusters.

Generative co-design of a ligand-binding protein pocket's residue sequence and full-atom structure, conditioned on the target small molecule.

Protein large language model adapted from LLaMA-2 that unifies sequence generation and superfamily classification in one 7B-parameter framework.

All-atom 3D molecular foundation model pretrained across small molecules, proteins, and complexes with an E(3)-equivariant denoising objective.

Compound-protein interaction prediction coupling a chemical language model to a protein language model via a cross-attention block.

De novo protein backbone diffusion model reaching 92% designability without structure-prediction pretraining, sampling in 100 steps on one GPU.

Protein-ligand binding affinity prediction from an amino acid sequence and a ligand SMILES string, with no structure, docked pose, or pocket needed.