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Comparez modèles biologiques, moteurs de docking et flux de calcul côte à côte. Filtrez par modèle d'accès, empreinte de calcul et catégorie pour parvenir à une table de décision exploitable en moins d'une minute.
| Attribut | ＋ Ajouter un outil | |||||
|---|---|---|---|---|---|---|
| Category | Biological Foundation Models | Biological AI Models | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Antibody Design | Protein Design | Protein Design | Protein Design | Protein Design | — |
| Company | Tsinghua University (AIR) | 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 | MIT 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 GPU (>= 16GB VRAM) | 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 | Antibody CDR design, therapeutic optimization, and paratope engineering | Non enregistré | Non enregistré | Non enregistré | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | — |
| Research workflow | Target antigen PDB -> generate complementary CDR conformations and sequences | Non enregistré | Non enregistré | Non enregistré | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | — |
| Evidence level | Peer-reviewed research (ICLR 2023) | 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 GitHub | Public research method/code | Public research method/code | Public research method/code | DeepMind research publication and ongoing academic collaboration | — |
| Features | Joint structure-sequence diffusion on antibody CDR-H3 and light chain loops, Antigen-conditioned generation with physical clash penalties, Benchmark evaluations showing higher affinity and diversity than RosettaAntibody, Open source codebase with PyTorch and PyG implementations | 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 | Equivariant Generative Diffusion | Published biomedical AI method | Published biomedical AI method | Published biomedical AI method | De Novo Binder Design | — |
| Input data | Non enregistré | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | Target atomic geometry; designed region and constraints | Non enregistré | — |
| Output data | Non enregistré | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | Generated protein all-atom structures | Non enregistré | — |
| Validation | Non enregistré | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Non enregistré | — |
| Type | Equivariant diffusion model | Non enregistré | Non enregistré | Non enregistré | Generative protein binder design model | — |
| Intended use | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Compatibility | Non enregistré | Non enregistré | Non enregistré | Non enregistré | Non enregistré | — |
| Manufacturer | Tsinghua University | Non enregistré | Non enregistré | Non enregistré | Google DeepMind | — |
| Integration evidence | https://github.com/luost26/diffab | Non enregistré | Non enregistré | Non enregistré | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | — |
| Laboratory handoff | Compatible with phage display and high-throughput antibody expression | Non enregistré | Non enregistré | Non enregistré | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | — |