All Competitors

Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.

Protein sequence design model that represents small molecules, nucleotides, and metals at atomic resolution, enabling ligand-aware enzyme design.

Protein language model that distills structure tokens into ESM2, yielding structurally enriched embeddings from sequence input alone.

De novo protein backbone generator trained on low-confidence AlphaFold structures as corrupted data, reaching 86% designability at 700 residues.

Protein conformational motion prediction from a single structure, using an SE(3)-equivariant GNN trained on ensembles mined from the PDB.

Protein sequence-structure encoder biasing attention with relative residue-frame transforms, for inverse folding and peptide binding prediction.

Protein conformational ensemble generator that denoises backbone geometry under language-model sequence conditioning with locality-aware attention.

Generative antibody and nanobody design model that co-designs CDR sequences and antigen-bound structures for de novo design and affinity maturation.

Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.

Tertiary structure-based RNA design model that fuses RNA backbone geometry with protein language model features of the bound partner protein.

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

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

Structure accuracy estimation over atom graphs, scoring macrocyclic peptide and protein-DNA models that residue-level predictors cannot represent.

SE(3)-invariant masked autoencoder that learns protein fold representations from AlphaFold-DB structures, supporting zero-shot fold classification.

Multimodal scientific foundation model unifying protein, DNA/RNA, and small-molecule structure in one token vocabulary for cross-domain reasoning.

Multimodal diffusion model that co-designs protein sequence and 3D structure around cofactors and small molecules for de novo heme enzyme design.

Protease inhibitor prediction for small secreted proteins lacking an inhibitor domain, pairing protein language models with structure filtering.

Structure-based drug design model that generates ligands for a protein pocket, pairing a diffusion structure encoder with preference optimization.

Protein backbone generation from voxelized 3D densities, pairing a 3D CNN autoencoder with latent flow matching for shape and motif conditioning.

Multimodal 80B-parameter protein-language model that answers natural language questions about protein function from sequence and structure.

Dual-target structure-based drug design that fuses two pocket-conditioned Bayesian flow distributions to generate 3D ligands binding both proteins.

Structure-based drug design framework pairing pharmacophore-guided latent diffusion with training-free, pocket-aware evolutionary optimization.

Sparse all-atom denoising models for de novo protein backbone generation, producing designable structures up to 1,000 residues in seconds.