Protein Design
Design, diversify, and score your proteins with the RFdiffusion pipeline. Create novel proteins or optimize existing ones with a streamlined process.
Watson et. al.
Design de novo protein binders against a target
Candido et al. 2026
Protein structure to sequence prediction
Dauparas et. al
Design ligand-binding proteins with RFdiffusion All Atom
Krishna et. al.
De novo binder design for nanobodies, antibodies, proteins, peptides, and cyclotides
BoltzGen et. al
De novo binder design
Lin et. al
Design macrocyclic proteins
Rettie et. al
Design de novo binders for your target
Pacesa et. al
Fully atomistic protein and ligand binder design
Didi et. al
Generate antibodies optimized for a given property
Gruver et. al
Design peptide binders by mimicking a known binder's interface
Kong et. al
All atom protein design with atom-level and residue-level motif scaffolding
Butcher et. al
Enzyme active site scaffolding with atom-level or residue-level motif specification
Ahern et. al
De novo antibody design
Santiago et. al
In vitro validated antibody design against antigens
Shanehsazzadeh et. al
De novo protein binder design using diffusion models
ByteDance et. al
Protein structure to sequence design with full-atom packed outputs
Shuai et. al
Design sequences for antibodies
Hummer et. al
VHH binder design
Swanson et. al
Design property-aware CDR sequence/structure
Villegas-Morcillo et. al
Language models to recommend mutations for increased antibody binding affinity
Hie et. al
Design or diversify proteins
Watson et. al
Motif scaffolding protein design
Yim et. al
Predict stability of point mutations
Dutton et. al
Antibody inverse folding
Branson et. al
Inverse folding with modeling of small molecules and more
Invert Boltz-1 to design protein binders
Cho et. al
Conditionally generate artificial enzymes
Illanes-Vicioso et. al
De novo protein design
Generate mutations with directed evolution
Emami et. al
Design cyclic peptides
Mutate protein complexes with structure-informed language model
Varun R. Shanker et al. et. al
Sequence design for protein, RNA, DNA, and mixed-polymer structures; protein-DNA binding specificity prediction
Kubaney et. al
Genetic algorithm-based protein binder optimization pipeline
Goudy OJ et. al
Generate sequences conditioned on existing sequence
Kevin K. Yang et. al
Potts model-based protein sequence design method that can condition on structural ensembles
Richard W. Shuai et. al
Efficient binder design
Frank et. al
(Re)Generate a binding pocket for a given small molecule
Zhang et. al
Solubilize membrane proteins
Protein design with Chai verification
Generate sequences given structure via reversing AlphaFold
Hybrid antibody design using AbLang + ProteinMPNN ensemble
del Alamo et. al
Optimize antibody/nanobody sequence affinity
Ruffolo et. al
Design thermostable proteins
Ertelt et. al
Antibody sequence design
Dreyer et. al
All-Atom Protein Generative Model
Wu et. al
Generate backbone fragments from a theozyme to build a motif library
Braun et. al
ML-assisted Directed Evolution
J. Funk et. al
Motif-conditioned functional enzyme sequence and structure co-design
Song et al.
Model-guided directed evolution workflow for multi-mutant nomination and assembly design
Tran et. al
Structure-based TCR design for peptide-MHC targets
Bradley et. al
Gradient-based binder design using Boltz-2
Nick Boyd et. al
Gradient-based binder design using Protenix
Design stable cyclic peptide sequences for a given backbone
Powers et. al
Evolve antibody sequences along realistic affinity-maturation trajectories
Lu et. al
Design sequences compatible with multiple conformational states
Abrudan et. al
Antibody CDR design with ODesign all-atom generative model
Diffusion-based protein sequence-structure co-design conditioned on ligands, DNA, or RNA
Rector-Brooks et. al
Diffusion-based de novo design of RNA, DNA, and nucleoprotein complex structures
Favor et. al