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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 Foundation Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Language Models | Protein Design | Protein Design | — |
| Company | University of Bayreuth | Pacesa Lab | Boltz Team | — |
| Access mode | Open Source | Research code or publication | Research code or publication | — |
| Licence conditions | Apache 2.0 | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | Standard GPU or multi-core CPU for inference | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | De novo protein sequence generation and enzyme design | 未明确记录 | 未明确记录 | — |
| Research workflow | Prompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold | 未明确记录 | 未明确记录 | — |
| Evidence level | Peer-reviewed research (Nature Communications 2022) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Open source release | Unknown, requires audit | Unknown, requires audit | — |
| Price | Free Open Source | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Available on Hugging Face and GitHub | Public research method/code | Public research method/code | — |
| Features | Autoregressive unconditional and conditional sequence generation, Produces sequences with natural secondary structure propensities and globular folding, Trained on UniRef50 with zero structural supervision required, Available on Hugging Face Model Hub with straightforward pipeline inference | 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 | AI Model | Research AI model / method | Research AI model / method | — |
| AI role | Generative Sequence Modeling | 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 | Autoregressive protein transformer | 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 | University of Bayreuth | 未明确记录 | 未明确记录 | — |
| Integration evidence | https://huggingface.co/nferruz/ProtGPT2 | 未明确记录 | 未明确记录 | — |
| Laboratory handoff | Synthesized sequences experimentally confirmed to adopt stable folds | 未明确记录 | 未明确记录 | — |