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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 | Drug Discovery & Chemistry | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Molecular Docking | Research AI Methods | Molecular Property Prediction | — |
| Company | Scripps Research Institute | Research / method authors | Luo Lab | — |
| Access mode | Open Source | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | Apache 2.0 | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Multi-core CPU (GPU-accelerated version available via QuickVina/Vina-GPU) | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Virtual screening of compound libraries and binding mode prediction | 未明确记录 | 未明确记录 | — |
| Research workflow | Input receptor PDBQT and ligand PDBQT -> output binding poses and energy scores | 未明确记录 | 未明确记录 | — |
| Evidence level | Peer-reviewed research with >25,000 scientific citations | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | — |
| Commercial eligibility | Permissive open source release | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Free Open Source | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Available on GitHub and Scripps website | Method paper or official documentation | Public research method/code | — |
| Features | High-speed multi-threaded molecular docking of flexible small-molecule ligands, Support for flexible receptor side chains and macrocyclic ligands, Python bindings (AutoDock-Vina Python package) for automated screening pipelines, Extensively integrated with open-source computational chemistry workflows | 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 | Open Source Software | Source-scoped research method | Research AI model / method | — |
| AI role | Empirical & Algorithmic Docking | ENSEMBLE_ML | Published biomedical AI method | — |
| Input data | 未明确记录 | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | — |
| Output data | 未明确记录 | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | — |
| Validation | 未明确记录 | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Molecular docking software | 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 | — |
| Manufacturer | The Scripps Research Institute | 未明确记录 | 未明确记录 | — |
| Integration evidence | https://vina.scripps.edu | 未明确记录 | 未明确记录 | — |
| Laboratory handoff | Identified hits ordered from commercial vendors (Enamine, ChemBridge) for in vitro validation | 未明确记录 | 未明确记录 | — |