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| Attribut | ＋ Werkzeug hinzufügen | |||
|---|---|---|---|---|
| Category | Biological Foundation Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Antibody Design | Protein Design | Protein Design | — |
| Company | University of Oxford (OPIG) | Pacesa Lab | Boltz Team | — |
| Access mode | Open Source | Research code or publication | Research code or publication | — |
| Licence conditions | BSD-3-Clause | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | 1x GPU or multi-core CPU | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Antibody humanization, CDR affinity maturation, and de novo paratope design | Nicht erfasst | Nicht erfasst | — |
| Research workflow | Input antibody backbone structure -> output high-affinity, humanized sequence candidates | Nicht erfasst | Nicht erfasst | — |
| Evidence level | Peer-reviewed publication (Nature Communications 2024) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Open source permissive | Unknown, requires audit | Unknown, requires audit | — |
| Price | Free Open Source | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Available on GitHub and PyPI | Public research method/code | Public research method/code | — |
| Features | Specialized inverse folding model for antibody heavy and light chains, Trained on non-redundant experimental structures from SAbDab and AlphaFold-Multimer predictions, Generates antibody sequence libraries with high stability and developability profiles, Fast inference suitable for screening millions of candidate CDR loops | AF2-guided optimization pipeline proposes protein binder designs for target structures., Candidate binder sequences, modeled protein complexes | Target-conditioned protein and peptide binder generation model as reported in its preprint., Binder sequence and structure candidates | — |
| Entry type | AI Model | Research AI model / method | Research AI model / method | — |
| AI role | Antibody Optimization | Published biomedical AI method | Published biomedical AI method | — |
| Input data | Nicht erfasst | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | Nicht erfasst | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | — |
| Validation | Nicht erfasst | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | — |
| Type | Antibody inverse folding model | Nicht erfasst | Nicht erfasst | — |
| Intended use | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Compatibility | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Manufacturer | University of Oxford | Nicht erfasst | Nicht erfasst | — |
| Integration evidence | https://github.com/oxpig/AntiFold | Nicht erfasst | Nicht erfasst | — |
| Laboratory handoff | Candidates compatible with high-throughput antibody expression and SPR binding assays | Nicht erfasst | Nicht erfasst | — |