Loading BioAtlas...
Loading BioAtlas...
مقایسه شاخ‌به‌شاخ مدل‌های زیستی، موتورهای داکینگ و پایپ‌لاین‌های محاسباتی. فیلتر بر اساس مدل دسترسی، مصرف پردازشی و دسته‌بندی جهت اخذ تصمیم دقیق در کمتر از یک دقیقه.
| مشخصه | ＋ افزودن ابزار | |||||
|---|---|---|---|---|---|---|
| Category | Biological Foundation Models | Biological AI Models | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Biomolecular Complex Prediction | Protein Design | Protein Design | Protein Design | Protein Design | — |
| Company | Baidu PaddleHelix | Pacesa Lab | Boltz Team | Baker Lab | Google DeepMind | — |
| Access mode | Open Source & Web Service | Research code or publication | Research code or publication | Research code or publication | Research Preview | — |
| Licence conditions | Apache 2.0 (code) / Model License | Check code and model weights separately | Check code and model weights separately | Check code and model weights separately | Research Publication / DeepMind Research | — |
| Compute requirements | 1x NVIDIA A100 or Kunlunxin AI accelerators | 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 | Target-ligand interaction modeling, RNA-protein complex prediction, and covalent drug discovery | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | — |
| Research workflow | Input biomolecular sequence and SMILES -> generate 3D complex coordinates and confidence scores | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | — |
| Evidence level | Preprint and blind benchmark evaluation (2024) | Peer-reviewed primary method | Primary research preprint | Primary research preprint | DeepMind Research Whitepaper & arXiv preprint (2024) | — |
| Commercial eligibility | Open research access with enterprise private deployment | Unknown, requires audit | Unknown, requires audit | Unknown, requires audit | Research publication & early academic collaboration | — |
| Price | Free Open Source / Enterprise Support | Check selected model licence | Check selected model licence | Check selected model licence | Research Preview | — |
| Availability ⓘ | Available globally on GitHub | Public research method/code | Public research method/code | Public research method/code | DeepMind research publication and ongoing academic collaboration | — |
| Features | Unified prediction of protein-ligand, protein-nucleic acid, and multi-chain assemblies, High-throughput distributed inference accelerated by PaddleHelix engine, Includes specialized chemical bond loss for precise covalent drug interactions, Openly accessible web server and downloadable weights for enterprise deployment | 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 / Web Server | Research AI model / method | Research AI model / method | Research AI model / method | AI Model | — |
| AI role | Structural Complex Modeling | Published biomedical AI method | Published biomedical AI method | Published biomedical AI method | De Novo Binder Design | — |
| Input data | ثبت‌نشده | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | Target atomic geometry; designed region and constraints | ثبت‌نشده | — |
| Output data | ثبت‌نشده | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | Generated protein all-atom structures | ثبت‌نشده | — |
| Validation | ثبت‌نشده | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | ثبت‌نشده | — |
| Type | All-atom structure prediction model | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | Generative protein binder design model | — |
| Intended use | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Compatibility | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Manufacturer | Baidu Inc. | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | Google DeepMind | — |
| Integration evidence | https://github.com/PaddlePaddle/PaddleHelix | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | — |
| Laboratory handoff | Structures inform cryo-EM model fitting and crystallographic phasing | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | — |