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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |
|---|---|---|
| Category | Biological Foundation Models | — |
| Subcategory | Antibody Design | — |
| Company | University of Oxford (OPIG) | — |
| Access mode | Open Source | — |
| Licence conditions | BSD-3-Clause | — |
| Compute requirements | 1x GPU or multi-core CPU | — |
| Biological application | Antibody humanization, CDR affinity maturation, and de novo paratope design | — |
| Research workflow | Input antibody backbone structure -> output high-affinity, humanized sequence candidates | — |
| Evidence level | Peer-reviewed publication (Nature Communications 2024) | — |
| Commercial eligibility | Open source permissive | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on GitHub and PyPI | — |
| 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 | — |
| Entry type | AI Model | — |
| AI role | Antibody Optimization | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | Antibody inverse folding model | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | University of Oxford | — |
| Integration evidence | https://github.com/oxpig/AntiFold | — |
| Laboratory handoff | Candidates compatible with high-throughput antibody expression and SPR binding assays | — |