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Confronta modelli biologici, motori di docking e flussi di calcolo affiancati. Filtra per modello di accesso, impronta di calcolo e categoria per ottenere una tabella decisionale attuabile in meno di un minuto.
| Attributo | ＋ Aggiungi strumento | |||
|---|---|---|---|---|
| Category | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Research AI Methods | Research AI Methods | Molecular Property Prediction | — |
| Company | Research / method authors | Research / method authors | Luo Lab | — |
| Access mode | Confirm distribution and exact release | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Version and data dependent | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Non registrato | Non registrato | Non registrato | — |
| Research workflow | Non registrato | Non registrato | Non registrato | — |
| Evidence level | PEER_REVIEWED_METHOD_OR_CONFERENCE | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Verify access and licence | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Method paper or official documentation | Method paper or official documentation | Public research method/code | — |
| Features | TargetDiff scientific method, Protein target pocket atom geometry, 3D diffusion-based conditional candidate molecules | 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 | — |
| Entry type | Source-scoped research method | Source-scoped research method | Research AI model / method | — |
| AI role | DEEP_LEARNING | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Protein target pocket atom geometry | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | 3D diffusion-based conditional candidate molecules | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | — |
| Validation | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Non registrato | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |