All Competitors

Hallucination-based multichain protein design framework that co-folds chains and forms the inter-chain interfaces of dimers, trimers, and tetramers.

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

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

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

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

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

Protein inverse folding model aligning ProteinMPNN by multi-objective preference optimization to improve developability without losing fold fidelity.

Full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.

De novo protein binder design suite from ByteDance pairing diffusion and hallucination generators with confidence-based filtering of designs.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

All-atom protein design framework that co-optimizes sequence and backbone by hallucinating with AlphaFold3-style structure predictors, no fine-tuning.

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

Protein binder design model post-trained from a multimodal protein language model to bind proteins, peptides, small molecules, and nucleic acids.