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Comparez modèles biologiques, moteurs de docking et flux de calcul côte à côte. Filtrez par modèle d'accès, empreinte de calcul et catégorie pour parvenir à une table de décision exploitable en moins d'une minute.
| Attribut | ＋ Ajouter un outil | |||||
|---|---|---|---|---|---|---|
| Category | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Molecular Docking | Research AI Methods | Molecular Property Prediction | Molecular Design | Research AI Methods | — |
| Company | DiffDock authors and contributors | Research / method authors | Luo Lab | Sekijima Lab | Research / method authors | — |
| Access mode | Public code / project terms | Confirm distribution and exact release | Research code or publication | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Review the linked release, model weights, datasets and service terms separately | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Consult linked installation guidance; workload dependent | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Research workflow | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Evidence level | Project or provider documentation; no local performance benchmark | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | Peer-reviewed primary method | OFFICIAL_DOCUMENTATION | — |
| Commercial eligibility | Not independently confirmed | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | See project terms; compute costs may apply | Verify access and licence | Check selected model licence | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | See project access and maintenance status | Method paper or official documentation | Public research method/code | Public research method/code | Method paper or official documentation | — |
| Features | Diffusion-based protein-ligand docking, Input: Protein structure or sequence and ligand SMILES or structure, Output: Candidate binding poses and confidence scores | ADMET-AI scientific method, Molecular SMILES with well-defined compound stereochemistry and relevant assay domain, Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Chemical foundation model for learned molecular representations, property prediction and conditional molecule generation., Molecular features, predicted properties or generated SMILES | Diffusion-based ligand molecule generation constrained by specified pocket and pharmacophore interactions., Generated 3D chemical candidate structures | DiffSBDD scientific method, Protein binding pocket coordinates and desired molecular design context, Diffusion-generated candidate ligands | — |
| Entry type | Research software / model | Source-scoped research method | Research AI model / method | Research AI model / method | Source-scoped research method | — |
| AI role | Scientific machine learning | ENSEMBLE_ML | Published biomedical AI method | Published biomedical AI method | DEEP_LEARNING | — |
| Input data | Protein structure or sequence and ligand SMILES or structure | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | Protein pocket coordinates; interaction constraints | Protein binding pocket coordinates and desired molecular design context | — |
| Output data | Candidate binding poses and confidence scores | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | Generated 3D chemical candidate structures | Diffusion-generated candidate ligands | — |
| Validation | Evaluate on representative data; no clinical suitability established | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Intended use | Biological and pharmaceutical research | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Compatibility | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Limitations | The repository defaults to DiffDock-L; a docking pose is not evidence of binding affinity. | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |