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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 | Biological Foundation Models RFdiffusion All-Atom (RFdiffusionAA)Institute for Protein Design (Baker Lab)开源代码⚡ A100/H100 | ＋ 添加工具 | ||
|---|---|---|---|---|
| Category | Biological Foundation Models | Biological AI Models | Biological AI Models | — |
| Subcategory | De Novo Protein Design | Protein Design | Protein Design | — |
| Company | Institute for Protein Design (Baker Lab) | Pacesa Lab | Boltz Team | — |
| Access mode | Academic Open Source | Research code or publication | Research code or publication | — |
| Licence conditions | Open Source for academic research; commercial license required for for-profit use | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | 1x NVIDIA A100 or RTX 4090 GPU (>= 24GB VRAM) | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Enzyme active site design, small molecule sensor engineering, and drug receptor design | 未明确记录 | 未明确记录 | — |
| Research workflow | Diffusion of protein backbone and sidechains around explicit ligand coordinates | 未明确记录 | 未明确记录 | — |
| Evidence level | Peer-reviewed research (Science 2024) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Commercial licensing available through University of Washington CoMotion | Unknown, requires audit | Unknown, requires audit | — |
| Price | Free for academic research / Paid commercial license | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Available on GitHub | Public research method/code | Public research method/code | — |
| Features | De novo design of protein binding pockets around specified small molecules, Co-generation of protein binders with coordinated catalytic ions and cofactors, Full atomic coordinate representation during the reverse diffusion trajectory, Seamless integration with LigandMPNN for sequence design | 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 / Software | Research AI model / method | Research AI model / method | — |
| AI role | De Novo Molecular Diffusion | 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 | Generative all-atom diffusion 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 | — |
| Manufacturer | Institute for Protein Design | 未明确记录 | 未明确记录 | — |
| Integration evidence | https://github.com/baker-laboratory/rf_diffusion_all_atom | 未明确记录 | 未明确记录 | — |
| Laboratory handoff | Designs validated via yeast surface display and biolayer interferometry (BLI) | 未明确记录 | 未明确记录 | — |