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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |||
|---|---|---|---|---|
| Category | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Molecular Property Prediction | Research AI Methods | Molecular Design | — |
| Company | Luo Lab | Research / method authors | Sekijima Lab | — |
| Access mode | Research code or publication | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Research workflow | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Evidence level | Peer-reviewed primary method | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Check selected model licence | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Public research method/code | Method paper or official documentation | Public research method/code | — |
| Features | Chemical foundation model for learned molecular representations, property prediction and conditional molecule generation., Molecular features, predicted properties or generated SMILES | ADMET-AI scientific method, Molecular SMILES with well-defined compound stereochemistry and relevant assay domain, Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Diffusion-based ligand molecule generation constrained by specified pocket and pharmacophore interactions., Generated 3D chemical candidate structures | — |
| Entry type | Research AI model / method | Source-scoped research method | Research AI model / method | — |
| AI role | Published biomedical AI method | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | Valid SMILES; trained downstream endpoint or conditioned molecule task | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Protein pocket coordinates; interaction constraints | — |
| Output data | Molecular features; predicted properties or generated SMILES | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Generated 3D chemical candidate structures | — |
| Validation | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Intended use | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Compatibility | 未明确记录 | 未明确记录 | 未明确记录 | — |