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.
Constraint-based multi-objective sequence design (Proto optimizer)
Merchant 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.
Open-source de novo antibody design
Han 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
Sequence design with Potts pairwise interactions
Birnbaum and Keating
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.
Peptide-MHC complex structure prediction and peptide design.
Asgary AH 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.