ImmuneBuilder
OPIG (Oxford)
Suite for predicting 3D structures of antibodies (ABodyBuilder), nanobodies (NanoBodyBuilder2), and TCRs (TCRBuilder2) from sequence.
Best For
Rapid antibody/nanobody/TCR structure prediction from sequence
License
Open Source (check repo)
Strengths
- +Covers antibodies, nanobodies, and TCRs
- +Fast inference
Limitations
- −CDR-H3 prediction remains challenging (~2.81 Å RMSD)
- −No full-length IgG
R&D Pipeline Coverage
Related Tools
IgFold
Johns Hopkins / Profluent Bio
Fast deep learning model for antibody structure prediction from sequence alone. Processes paired heavy/light chain inputs.
AntiFold
OPIG (Oxford)
Antibody-specific inverse folding model fine-tuned from ESM-IF1. Designs sequences predicted to maintain structural fold given an antibody backbone.
More in Antibody Design
DiffAb
Luo et al. (NeurIPS 2022)
Diffusion-based generative model that jointly designs antibody CDR sequences and 3D structures conditioned on antigen structure.
RFantibody
Baker Lab / IPD (University of Washington)
RFdiffusion fine-tuned for de novo antibody design. Generates VHHs, scFvs, and full antibodies targeting user-specified epitopes. Experimentally validated with cryo-EM.
AntiFold
OPIG (Oxford)
Antibody-specific inverse folding model fine-tuned from ESM-IF1. Designs sequences predicted to maintain structural fold given an antibody backbone.
Stay updated on ImmuneBuilder
Weekly newsletter covering AI tool releases, benchmarks, and what practitioners actually use.