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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 Design | Research AI Methods | Molecular Property Prediction | — |
| Company | AstraZeneca / MolecularAI contributors | 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 | 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 | 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 | Generative molecular design and optimization, Input: Molecular examples, objectives and scoring configuration, Output: Generated and ranked molecular candidates | 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 | Molecular examples, objectives and scoring configuration | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | Generated and ranked molecular candidates | 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 | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Intended use | Biological and pharmaceutical research | 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 | — |
| Limitations | Scoring objectives can bias outputs; synthesis feasibility and activity need separate testing. | 未明确记录 | 未明确记录 | — |