All Competitors

Protein language model that predicts per-residue local energetic frustration directly from sequence, covering whole proteomes and disordered regions.

Connects a frozen protein language model to a general LLM via a cross-modal projector, adding protein reasoning without catastrophic forgetting.

Full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.

Masked language model for T-cell receptor and peptide-MHC binding prediction, with compositional pretraining and non-autoregressive decoding.

Latent flow-matching method that repurposes protein language model embeddings to generate high-fitness protein variants without predictor guidance.

Causal 309M-parameter protein language model that scores variant fitness zero-shot and generates sequences, reaching 0.390 Spearman on ProteinGym.

Enzyme design model that jointly generates enzyme sequences and substrate-binding pockets, conditioned on functional priors and substrate structure.

Structure-based protein encoder that voxelizes every heavy atom into a 3D grid, learning orientation-robust representations for protein function.

Partially latent flow-matching model for de novo protein design, jointly generating sequence and all-atom structure for proteins up to 800 residues.

Flow-matching generative model for de novo protein binder backbone design, built on a Pairformer architecture with in silico interface maturation.

LoRA adapter on ProstT5 predicting per-residue distributions over Foldseek 3Di tokens, capturing conformational flexibility from MD trajectories.

Structure-free protein-ligand binding affinity predictor built on OpenFold3 that scores potency from a protein sequence and a ligand SMILES string.

Zero-shot peptide binder designer that runs diffusion in a pretrained protein embedding space, proposing binders without structure prediction.

Generative transformer for ancestral protein sequence reconstruction that needs no multiple sequence alignment or phylogenetic tree as input.

Evolution-guided diffusion model that generates temporal protein folding pathways, from unfolded chain to native state, rather than static structures.

Contrastive geometric model unifying structure- and ligand-based drug design for zero-shot virtual screening, target fishing, and pocket selection.

Metagenomic foundation model trained on 9.7 trillion nucleotide tokens for generative therapeutic design across genes, peptides, and microbiomes.

Diffusion model for de novo AAV capsid design that steers sampling with a viability classifier toward assemblable, packaging-competent variants.

Sequence-only latent diffusion model that designs target-specific peptide binders, cascaded with an affinity classifier through joint optimization.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

Antibody developability predictor pairing text and protein language models, using in-context learning to fit new assays without retraining.

Multimodal architecture coupling pretrained DNA, RNA, and protein language models with directional cross-attention into one Virtual Cell Embedding.

Peptide developability predictor scoring solubility, permeability, toxicity, and binding from amino-acid sequences or chemically modified SMILES.

All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.