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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 Design | Protein Design | Protein Design | — |
| Company | Microsoft Research / EvoDiff authors | Pacesa Lab | Boltz Team | — |
| Access mode | Public code / project terms | Research code or publication | Research code or publication | — |
| Licence conditions | Review code, weights, data and service terms separately | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | See source requirements for selected configuration | 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 | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Evidence level | Project/provider documentation; no local benchmark | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Not independently confirmed | Unknown, requires audit | Unknown, requires audit | — |
| Price | See project terms; compute costs may apply | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | See project release and maintenance status | Public research method/code | Public research method/code | — |
| Features | Protein sequence generation using discrete diffusion, Input: Sequence or alignment context and generation settings, Output: Candidate protein sequences | 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 | Research software / framework | Research AI model / method | Research AI model / method | — |
| AI role | Scientific machine learning | Published biomedical AI method | Published biomedical AI method | — |
| Input data | Sequence or alignment context and generation settings | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | Candidate protein sequences | 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 | 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 | Generated sequences need functional and expression testing; no biological performance guarantee. | 未明确记录 | 未明确记录 | — |