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Compare modelos biológicos, motores de docking y flujos computacionales lado a lado. Filtre por modelo de acceso, huella de cómputo y categoría para obtener una tabla de decisiones útil en menos de un minuto.
| Atributo | ＋ Añadir herramienta | |
|---|---|---|
| Category | Biological Foundation Models | — |
| Subcategory | Antibody Design | — |
| Company | Tsinghua University (AIR) | — |
| Access mode | Open Source | — |
| Licence conditions | MIT License | — |
| Compute requirements | 1x GPU (>= 16GB VRAM) | — |
| Biological application | Antibody CDR design, therapeutic optimization, and paratope engineering | — |
| Research workflow | Target antigen PDB -> generate complementary CDR conformations and sequences | — |
| Evidence level | Peer-reviewed research (ICLR 2023) | — |
| Commercial eligibility | Open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on GitHub | — |
| Features | Joint structure-sequence diffusion on antibody CDR-H3 and light chain loops, Antigen-conditioned generation with physical clash penalties, Benchmark evaluations showing higher affinity and diversity than RosettaAntibody, Open source codebase with PyTorch and PyG implementations | — |
| Entry type | AI Model | — |
| AI role | Equivariant Generative Diffusion | — |
| Input data | No registrado | — |
| Output data | No registrado | — |
| Validation | No registrado | — |
| Type | Equivariant diffusion model | — |
| Intended use | No registrado | — |
| Compatibility | No registrado | — |
| Manufacturer | Tsinghua University | — |
| Integration evidence | https://github.com/luost26/diffab | — |
| Laboratory handoff | Compatible with phage display and high-throughput antibody expression | — |