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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 | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Molecular Docking | Research AI Methods | Molecular Property Prediction | — |
| Company | EquiBind authors | Research / method authors | Luo Lab | — |
| Access mode | Public code / project terms | 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 | Consult linked installation guidance; workload dependent | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Not recorded | Not recorded | Not recorded | — |
| Research workflow | Not recorded | Not recorded | Not recorded | — |
| 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 | See project terms; compute costs may apply | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | See project access and maintenance status | Method paper or official documentation | Public research method/code | — |
| Features | Geometric deep-learning prediction of ligand binding poses, Input: Protein and small-molecule structures, Output: Predicted bound ligand coordinates | 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 | Research software / model | Source-scoped research method | Research AI model / method | — |
| AI role | Scientific machine learning | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Protein and small-molecule structures | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | Predicted bound ligand coordinates | 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 | Not recorded | Not recorded | Not recorded | — |
| Intended use | Biological and pharmaceutical research | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Limitations | Assess steric plausibility and target-specific performance before using poses downstream. | Not recorded | Not recorded | — |