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Vergleichen Sie biologische Modelle, Docking-Engines und Berechnungs-Workflows Seite an Seite. Filtern Sie nach Zugangsmodell, Rechenbedarf und Kategorie, um in unter einer Minute zu einer verwertbaren Entscheidungstabelle zu gelangen.
| Attribut | ＋ Werkzeug hinzufügen | |
|---|---|---|
| Category | Biological AI Models | — |
| Subcategory | Protein Design | — |
| Company | Pacesa Lab | — |
| Access mode | Research code or publication | — |
| Licence conditions | Check code and model weights separately | — |
| Compute requirements | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Nicht erfasst | — |
| Research workflow | Nicht erfasst | — |
| Evidence level | Peer-reviewed primary method | — |
| Commercial eligibility | Unknown, requires audit | — |
| Price | Check selected model licence | — |
| Availability ⓘ | Public research method/code | — |
| Features | AF2-guided optimization pipeline proposes protein binder designs for target structures., Candidate binder sequences, modeled protein complexes | — |
| Entry type | Research AI model / method | — |
| AI role | Published biomedical AI method | — |
| Input data | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | — |
| Output data | Candidate binder sequences; modeled protein complexes | — |
| Validation | Requires independent dataset-specific evaluation | — |
| Type | Nicht erfasst | — |
| Intended use | Nicht erfasst | — |
| Compatibility | Nicht erfasst | — |