All Competitors

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

Open-source all-atom co-folding foundation model for protein-ligand, protein-protein, and antibody-antigen complex prediction in drug discovery.

Atomic protein model building from cryo-EM density maps, resolving conformational heterogeneity through atom-centric sampling and diffusion.

Diffusion model for 3D small-molecule design against protein-protein interaction sites, guided by the natural binding peptide or protein partner.

Diffusion model that generates 3D small molecules conditioned on protein pockets and partial fragments encoded as continuous spatial density maps.

Small-molecule hit-discovery pipeline using Boltz-2 co-folding and affinity prediction to rank in-stock compounds or make-on-demand chemical space.

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

Diffusion-based generative RNA model for de novo sequence design, conditioned on function, RNA family, structure, or binding proteins.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

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

Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.

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

Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.

Masked discrete-diffusion model over millions of full-length mRNAs, steered by Monte Carlo tree search for joint codon optimization and UTR design.

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

Protein structure prediction and binder design in a single generative step, replacing AlphaFold3's iterative diffusion sampling with one forward pass.

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.

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.

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

Motif-aware graph diffusion model for controllable molecular generation that adapts to unseen properties by learning a lightweight task embedding.