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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 | Target Discovery | Research AI Methods | Molecular Property Prediction | — |
| Company | Insilico Medicine | Research / method authors | Luo Lab | — |
| Access mode | Provider account / licence | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | Review the linked release, model weights, datasets and service terms separately | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Confirm deployment and system requirements with provider | 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 | Project or provider documentation; no local performance benchmark | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | Not independently confirmed | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Contact provider for current pricing | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Provider access and terms required | Method paper or official documentation | Public research method/code | — |
| Features | AI-assisted target identification and prioritization, Input: Disease context and molecular evidence, Output: Ranked target hypotheses | 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 research software | Source-scoped research method | Research AI model / method | — |
| AI role | Scientific machine learning | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Disease context and molecular evidence | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | Ranked target hypotheses | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | — |
| Validation | Evaluate on representative data; no clinical suitability established | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Non registrato | Non registrato | Non registrato | — |
| Intended use | Biological and pharmaceutical research | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Limitations | Target prioritization does not establish causality or therapeutic efficacy; confirm data coverage and access. | Non registrato | Non registrato | — |