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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 | Drug Discovery & Chemistry Schrödinger Maestro & Computational Chemistry SuiteSchrödingerCommerciale⚡ A100/H100 | ＋ Aggiungi strumento | ||
|---|---|---|---|---|
| Category | Drug Discovery & Chemistry | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Physics & AI Drug Design Platform | Research AI Methods | Molecular Property Prediction | — |
| Company | Schrödinger | Research / method authors | Luo Lab | — |
| Access mode | Commercial License | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | Proprietary commercial license | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Workstation / high-performance GPU cluster (CUDA-enabled) | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Small-molecule drug discovery, structure-based design, and ADMET profiling | Non registrato | Non registrato | — |
| Research workflow | Hit-to-lead and lead optimization via FEP+ calculations and docking | Non registrato | Non registrato | — |
| Evidence level | Extensive peer-reviewed validation across hundreds of biopharma programs | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | Commercial enterprise licensing | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Enterprise license — contact vendor | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Globally available for commercial and academic institutions | Method paper or official documentation | Public research method/code | — |
| 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 | — |
| Entry type | Commercial Software | Source-scoped research method | Research AI model / method | — |
| AI role | Physics & ML Hybrid Modeling | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Non registrato | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | Non registrato | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | — |
| Validation | Non registrato | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Enterprise computational chemistry platform | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Manufacturer | Schrödinger, Inc. | Non registrato | Non registrato | — |
| Integration evidence | https://www.schrodinger.com/platform/products/maestro/ | Non registrato | Non registrato | — |
| Laboratory handoff | Direct compound prioritization for chemical synthesis and enzymatic assays | Non registrato | Non registrato | — |