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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |
|---|---|---|
| Category | Research Cloud & Data Platforms | — |
| Subcategory | Research Infrastructure | — |
| Company | Amazon Web Services | — |
| Access mode | Provider-specific terms | — |
| Licence conditions | AWS service terms and each model/data licence must be evaluated separately. | — |
| Compute requirements | Workflow-specific cloud instances, storage and orchestration; verify current instance availability and dependencies. | — |
| Biological application | Provider-documented protein folding workflows | — |
| Research workflow | Run protein-folding workflows in the cloud | — |
| Evidence level | Provider implementation example | — |
| Commercial eligibility | Not independently confirmed | — |
| Price | Price not recorded — check supplier | — |
| Availability ⓘ | Current availability and regional access not confirmed — check provider. | — |
| Features | Machine-learning development platform | — |
| Entry type | Software / platform | — |
| AI role | AI / computational workflow | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | Machine-learning development platform | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | Amazon Web Services | — |
| Integration evidence | https://aws.amazon.com/blogs/machine-learning/build-protein-folding-workflows-to-accelerate-drug-discovery-on-amazon-sagemaker/ | — |
| Laboratory handoff | Editorial next step: choose experimental validation based on the biological question. No automatic laboratory handoff is documented here. | — |