All Competitors

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

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

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

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.

A generative protein-protein docking model that adapts AlphaFold-Multimer via flow matching, replacing the template module with a docking module.

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

Enhanced 464M-parameter version of Protenix with substantial gains in antibody-antigen complex prediction over v1, plus target-conditioned VHH-Fc generative design with up to 48% hit rates.

Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.

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 paired-sequence protein language model that jointly encodes interacting proteins to predict interactions, binding affinity, and interface contacts.

Contrastive antibody language model that predicts antibody-antigen binding specificity directly from amino acid sequence using a dual-encoder, cross-attentive architecture.

A Pairformer-based flow-matching generative model for de novo protein binder backbone design, paired with in silico maturation to reach picomolar-to-nanomolar affinities.

Multimodal antibody developability predictor that combines text and protein language models with in-context learning to predict many properties without retraining.

A paired heavy/light antibody language model that fine-tunes ESM-2 and ESM-C with CDR-preferential masking, yielding zero-shot embeddings for binding-affinity prediction.

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

AlphaFold fine-tuned via OpenFold on 944 high-resolution MHC-peptide crystal structures, achieving median peptide RMSD of 0.65 Å on held-out complexes.

Generative language model trained on 558 million antibody sequences for infilling-based design of CDR loops and full-length immunoglobulin sequences.

Graph neural network framework for antigen-specific antibody CDR design, combining a pre-trained antibody language model with one-shot sequence and structure generation.

An unsupervised transformer language model for predicting TCR-epitope binding that generalizes to unseen epitopes by learning from incomplete immunological data.

An equivariant graph transformer trained with masked language modeling on protein structure to learn contextual amino acid encodings for sequence design and interface modeling.

Zero-shot antibody affinity maturation using ESM pseudolikelihood scoring. Improves binding up to 160-fold with no antigen-specific training data.

AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.

Reprograms a frozen English BERT model for antibody CDR sequence infilling via learnable cross-domain projection matrices, without training a new protein language model.