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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 | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Language Models | Protein Design | Protein Design | Protein Design | Protein Design | — |
| Company | University of Bayreuth | Pacesa Lab | Boltz Team | Baker Lab | Google DeepMind | — |
| Access mode | Open Source | Research code or publication | Research code or publication | Research code or publication | Research Preview | — |
| Licence conditions | Apache 2.0 | Check code and model weights separately | Check code and model weights separately | Check code and model weights separately | Research Publication / DeepMind Research | — |
| 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 | Check selected checkpoint and runtime; no GPU catalog recommendation | GPU cluster for generative diffusion sampling and candidate filtering | — |
| Biological application | De novo protein sequence generation and enzyme design | 未明确记录 | 未明确记录 | 未明确记录 | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | — |
| Research workflow | Prompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold | 未明确记录 | 未明确记录 | 未明确记录 | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | — |
| Evidence level | Peer-reviewed research (Nature Communications 2022) | Peer-reviewed primary method | Primary research preprint | Primary research preprint | DeepMind Research Whitepaper & arXiv preprint (2024) | — |
| Commercial eligibility | Open source release | Unknown, requires audit | Unknown, requires audit | Unknown, requires audit | Research publication & early academic collaboration | — |
| Price | Free Open Source | Check selected model licence | Check selected model licence | Check selected model licence | Research Preview | — |
| Availability ⓘ | Available on Hugging Face and GitHub | Public research method/code | Public research method/code | Public research method/code | DeepMind research publication and ongoing academic collaboration | — |
| 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 | All-atom protein structure diffusion conditioned on specific ligand or nucleic acid atomic context., Generated protein all-atom structures | Generates high-affinity protein binders for diverse target proteins without initial starting fragments, Trained on large macromolecular structure repositories and AlphaFold predictions, Demonstrates up to 300-fold higher binding affinities than prior computational design methods, Experimental success rates ranging from 9% to 88% across diverse target proteins in wet-lab biophysical assays | — |
| Entry type | AI Model | Research AI model / method | Research AI model / method | Research AI model / method | AI Model | — |
| AI role | Generative Sequence Modeling | Published biomedical AI method | Published biomedical AI method | Published biomedical AI method | De Novo Binder Design | — |
| Input data | 未明确记录 | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | Target atomic geometry; designed region and constraints | 未明确记录 | — |
| Output data | 未明确记录 | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | Generated protein all-atom structures | 未明确记录 | — |
| Validation | 未明确记录 | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | 未明确记录 | — |
| Type | Autoregressive protein transformer | 未明确记录 | 未明确记录 | 未明确记录 | Generative protein binder design model | — |
| Intended use | 未明确记录 | 未明确记录 | 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 | 未明确记录 | 未明确记录 | — |
| Manufacturer | University of Bayreuth | 未明确记录 | 未明确记录 | 未明确记录 | Google DeepMind | — |
| Integration evidence | https://huggingface.co/nferruz/ProtGPT2 | 未明确记录 | 未明确记录 | 未明确记录 | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | — |
| Laboratory handoff | Synthesized sequences experimentally confirmed to adopt stable folds | 未明确记录 | 未明确记录 | 未明确记录 | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | — |