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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 | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Structure | Protein Design | Protein Design | — |
| Company | Google DeepMind | Pacesa Lab | Boltz Team | — |
| Access mode | Provider-specific terms | Research code or publication | Research code or publication | — |
| Licence conditions | Code and model parameters have separate terms. Parameter access must be obtained from the provider; commercial eligibility is not established here. | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | H100 80 GB; CPU/RAM and sequence databases are also required. Larger inputs can require configuration changes. | Check selected checkpoint and runtime; no GPU catalog recommendation | 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 | Predict biomolecular structures | 未明确记录 | 未明确记录 | — |
| Evidence level | Provider-tested configuration | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Not independently confirmed | Unknown, requires audit | Unknown, requires audit | — |
| Price | See provider terms | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Check provider access requirements | Public research method/code | Public research method/code | — |
| Features | Biomolecular structure prediction, Local inference pipeline | AF2-guided optimization pipeline proposes protein binder designs for target structures., Candidate binder sequences, modeled protein complexes | Target-conditioned protein and peptide binder generation model as reported in its preprint., Binder sequence and structure candidates | — |
| Entry type | Software / platform | Research AI model / method | Research AI model / method | — |
| AI role | AI / computational workflow | Published biomedical AI method | Published biomedical AI method | — |
| Input data | 未明确记录 | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | 未明确记录 | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | — |
| Validation | 未明确记录 | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | — |
| Type | Structure prediction model | 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 | — |
| Access ⓘ | See code licence and model parameter terms | 未明确记录 | 未明确记录 | — |
| Integration evidence | https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md | 未明确记录 | 未明确记录 | — |
| Laboratory handoff | Editorial next step: select an appropriate structural or functional validation assay with the research team. No direct instrument integration has been verified. | 未明确记录 | 未明确记录 | — |