All Competitors

Variable-length generative protein design across structure, sequence, motif scaffolding, and peptide co-design via a generalized Poisson flow.

All-atom foundation model for immune-receptor design that predicts structures and co-designs CDR sequences for antibodies, nanobodies, and TCRs.

Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.

De novo protein binder and nanobody design pipeline that ranks candidates by a protein-protein interaction model rather than structural confidence.

Conditional denoising diffusion model that designs antigen-specific TCR CDR3β sequences conditioned on peptide-MHC targets and germline V-genes.

Protein binder generator producing receptor-conditioned binders from sequence alone, using a sparse Mixture-of-Experts transformer with no 3D input.

Reinforcement learning framework that fine-tunes the ProGen2-OAS antibody language model with GRPO to cut germline bias in generated sequences.

Message-passing neural network that designs buried hydrogen-bond networks onto protein backbones, combining learned placement with PyRosetta scoring.

Unified all-atom generative model for biomolecular structure prediction, binder filtering, and controllable protein and nanobody design.

Generative foundation model for antibody and multispecific design, doubling its predecessor's experimental success rate on therapeutic targets.

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

Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.

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

Structure-prediction and design engine that turns ESMC sequence representations into all-atom 3D structures of proteins and biomolecular complexes.

Dirichlet flow-matching model for protein design that generates family-aware sequences from ancestral-reconstruction priors, not random noise.

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

370M-parameter ligand-conditioned discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

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

Conditional discrete diffusion model for protein variant generation, with a calibrated identity dial controlling drift from a wild-type sequence.

Latent diffusion model for PTM-aware protein sequence design, using ControlNet-style conditioning to steer generation toward chosen PTM sites.

Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.

All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.

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

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