Loading BioAtlas...
Loading BioAtlas...
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 | Drug Discovery & Chemistry Schrödinger Maestro & Computational Chemistry SuiteSchrödingerKommerziell⚡ A100/H100 | ＋ Werkzeug hinzufügen | ||||
|---|---|---|---|---|---|---|
| Category | Drug Discovery & Chemistry | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Physics & AI Drug Design Platform | Research AI Methods | Molecular Property Prediction | Molecular Design | Research AI Methods | — |
| Company | Schrödinger | Research / method authors | Luo Lab | Sekijima Lab | Research / method authors | — |
| Access mode | Commercial License | Confirm distribution and exact release | Research code or publication | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Proprietary commercial license | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Workstation / high-performance GPU cluster (CUDA-enabled) | 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 | Small-molecule drug discovery, structure-based design, and ADMET profiling | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Research workflow | Hit-to-lead and lead optimization via FEP+ calculations and docking | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Evidence level | Extensive peer-reviewed validation across hundreds of biopharma programs | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | Peer-reviewed primary method | OFFICIAL_DOCUMENTATION | — |
| Commercial eligibility | Commercial enterprise licensing | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Enterprise license — contact vendor | Verify access and licence | Check selected model licence | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | Globally available for commercial and academic institutions | Method paper or official documentation | Public research method/code | Public research method/code | Method paper or official documentation | — |
| Features | Industry-leading FEP+ for predicting relative and absolute protein-ligand binding free energies, Glide docking engine with induced-fit and deep-learning scoring options, Maestro 3D visual modeling and pharmacophore hypothesis generation, Active learning molecular property prediction across multi-billion compound spaces | 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 | Commercial Software | Source-scoped research method | Research AI model / method | Research AI model / method | Source-scoped research method | — |
| AI role | Physics & ML Hybrid Modeling | ENSEMBLE_ML | Published biomedical AI method | Published biomedical AI method | DEEP_LEARNING | — |
| Input data | Nicht erfasst | 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 | Nicht erfasst | 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 | Nicht erfasst | 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 | Enterprise computational chemistry platform | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Intended use | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Compatibility | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Manufacturer | Schrödinger, Inc. | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Integration evidence | https://www.schrodinger.com/platform/products/maestro/ | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Laboratory handoff | Direct compound prioritization for chemical synthesis and enzymatic assays | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |