All Competitors

Peptide-focused instruction-tuned LLM that describes function, designs sequences, predicts eight bioactivity properties and edits physicochemistry.

Machine learning force field for all-atom protein dynamics, trained on 40 million DFT dipeptide conformations covering backbone and side-chain space.

Protein language model for microbial smORF-encoded small proteins, pairing multi-scale convolutions with transformer layers in a compact encoder.

Signal peptide design framework that generates, filters, and ranks cargo-specific secretion signals using evolution-constrained discrete diffusion.

Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.

Retrieval-augmented framework for de novo peptide binder design that conditions generation on retrieved, structurally aligned binding evidence.

Latent diffusion model that designs D-peptide binders against native L-protein targets, generalizing across chirality via axial vector features.

Zero-shot peptide binder designer that runs diffusion in a pretrained protein embedding space, proposing binders without structure prediction.

Sequence-only latent diffusion model that designs target-specific peptide binders, cascaded with an affinity classifier through joint optimization.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

Peptide developability predictor scoring solubility, permeability, toxicity, and binding from amino-acid sequences or chemically modified SMILES.

Peptide language model trained on HELM notation, a DeBERTa encoder for property prediction on macrocyclic and non-canonical medium-sized peptides.

Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.

GPCR structure prediction and peptide design model that generates linear and cyclic peptide agonists carrying noncanonical amino acids, zero-shot.

Amyloidogenicity predictor that classifies hexapeptides and scans whole proteins for aggregation-prone regions using frozen ESM-2 embeddings.

Flow matching model that builds any non-canonical amino acid into a protein pocket from its SMILES string, at 1.43 Å mean RMSD on held-out ncAAs.

Gradient-free protein design framework that treats engineering as Monte Carlo sampling of a user-defined energy landscape over pretrained models.

Protein structure prediction and peptide binder design model covering the 20 canonical amino acids plus 29 noncanonical residues.

Cyclic peptide binder design by Monte Carlo tree search over sequence space, scored by predicted confidence of the peptide-target complex fold.

Dual-target protein sequence design conditioned on two receptor structures at once, combining a heterogeneous graph network with ESM-2 features.

Cyclic peptide structure prediction for sequences carrying unnatural amino acids, adding atom-level features and cyclization-aware position encoding.

Surrogate machine learning force field reusing a reference model's node features from earlier steps, running molecular dynamics eight times faster.

De novo cyclic peptide binder design against a protein target, chaining a cyclized diffusion sampler, sequence design and structure prediction.