All Competitors

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

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

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

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

Protein-family language model trained on unaligned homolog sets for zero-shot variant fitness prediction and design. ProFam-1 holds 251M parameters.

De novo protein binder design suite from ByteDance pairing diffusion and hallucination generators with confidence-based filtering of designs.

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.

Structure-conditioned protein sequence design, pairing a three-track architecture with discrete flow matching for fast, few-step inverse folding.

Retrieval-augmented diffusion model for protein inverse folding that conditions sequence generation on profiles from structurally similar homologs.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

Guided discrete diffusion model for antibody lead optimization, conditioning sequence design on the seed binder's CDR canonical backbone conformation.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

Enzyme backbone design model that adds substrate and catalytic-site control to a pretrained SE(3) flow-matching generator via a lightweight adapter.

Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.

All-atom generative model for de novo protein design using SE(3) flow matching over oriented residue rigid bodies.

Suite of large language models fine-tuned with LoRA to generate antigen-targeted antibody Fv sequences from an antigen and its epitope.

Proprietary family of multimodal protein language models (up to 102B parameters) for sequence and structure generation, editing, and design.

Retrieval-augmented inverse folding model that fuses structural motif retrieval with a hybrid attention decoder to design sequences for a backbone.

Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.

Protein structure autoencoder compressing backbone coordinates into a latent space, paired with a latent diffusion model for generative design.

Autoregressive protein language model for antibody Fc domains, reinforcement-tuned to design variants with programmable Fc-receptor binding profiles.

Protein foundation model with 3B parameters, pretrained jointly on sequence and 3D structure via masked language modeling and diffusion denoising.

Diffusion model over coarse-grained protein topology that samples diverse folds, handing them to RFdiffusion to build de novo protein backbones.