All Competitors

Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.

A protein-text foundation model embedding sequences and natural language in a shared token space, enabling protein understanding and de novo design from one checkpoint.

Biohub's 2026 protein language model trained on ~2.8 billion sequences, forming the representation core of its world model of protein biology.

Biohub's structure-prediction and design engine that turns ESMC sequence representations into atomic-resolution 3D structures of proteins and biomolecular complexes.

A Dirichlet flow-matching model that generates family-aware protein sequences by initializing from ancestral-reconstruction lineage priors rather than random noise.

A ligand-conditioned masked discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Multiscale graph neural network for fixed-backbone protein binder sequence design with a contrastive decoding algorithm to improve target selectivity.

An 85M-parameter conditional discrete diffusion model for protein variant generation with a calibrated identity dial to steer similarity to a wild-type sequence.

A latent diffusion model with ControlNet-style conditioning for post-translational-modification-aware protein sequence design.

Discrete diffusion model for conditional antibody sequence generation that restricts learning to somatic variation via a germline-absorbing noising process.

A fully atomic protein co-design model using unified multimodal diffusion to jointly refine atom types and coordinates in a single stage, with support for non-canonical amino acids.

Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.

A co-generative protein language model that jointly decodes sequence and structure tokens from GO functional annotations for de novo functional protein design.

A reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal-LLM understanding expert with a Boltz-1-based diffusion generation expert.

Generative multimodal foundation model that jointly models DNA, RNA, protein, and cellular context across six biological modalities, with SOTA splicing prediction.

An encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble biophysical descriptors.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

Autoregressive language model trained on 37 million intrinsically disordered region sequences from the AlphaFold Database, generating IDR sequences conditioned on surrounding structured context.

Physics-informed generative model that pairs flow matching with a Mamba state-space backbone for linear-time protein backbone design, scaling to 2,000+ residues.

All-atom generative foundation model trained on 5M+ biomolecular complexes that designs small molecules, peptides, and nanobodies against a target site from one checkpoint.

Edit-based flow-matching model that generates variable-length protein variants via learned insertions, deletions, and substitutions on a template sequence.

A neural Hamiltonian flow that generates protein sequences with continuous, inference-time control over composition and net charge via analytical bias potentials—no retraining required.

Property-driven protein inverse folding: a ProteinMPNN checkpoint aligned via multi-objective preference optimization to improve developability while preserving structural fidelity.