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| Attribut | ＋ Werkzeug hinzufügen | |||||
|---|---|---|---|---|---|---|
| Category | Biological AI Models | Biological AI Models | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Structure | Protein Design | Protein Design | Protein Design | Protein Design | — |
| Company | Chai Discovery | Pacesa Lab | Boltz Team | Baker Lab | Google DeepMind | — |
| Access mode | Public code / project terms | Research code or publication | Research code or publication | Research code or publication | Research Preview | — |
| Licence conditions | Review the linked release, model weights, datasets and service terms separately | Check code and model weights separately | Check code and model weights separately | Check code and model weights separately | Research Publication / DeepMind Research | — |
| Compute requirements | Consult linked installation guidance; workload dependent | 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 | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | — |
| Research workflow | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | — |
| Evidence level | Project or provider documentation; no local performance benchmark | Peer-reviewed primary method | Primary research preprint | Primary research preprint | DeepMind Research Whitepaper & arXiv preprint (2024) | — |
| Commercial eligibility | Not independently confirmed | Unknown, requires audit | Unknown, requires audit | Unknown, requires audit | Research publication & early academic collaboration | — |
| Price | See project terms; compute costs may apply | Check selected model licence | Check selected model licence | Check selected model licence | Research Preview | — |
| Availability ⓘ | See project access and maintenance status | Public research method/code | Public research method/code | Public research method/code | DeepMind research publication and ongoing academic collaboration | — |
| Features | Biomolecular complex structure prediction, Input: Protein and nucleic-acid sequences, ligand descriptions and optional restraints, Output: Predicted molecular complex structures | 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 | Research software / model | Research AI model / method | Research AI model / method | Research AI model / method | AI Model | — |
| AI role | Scientific machine learning | Published biomedical AI method | Published biomedical AI method | Published biomedical AI method | De Novo Binder Design | — |
| Input data | Protein and nucleic-acid sequences, ligand descriptions and optional restraints | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | Target atomic geometry; designed region and constraints | Nicht erfasst | — |
| Output data | Predicted molecular complex structures | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | Generated protein all-atom structures | Nicht erfasst | — |
| Validation | Evaluate on representative data; no clinical suitability established | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Nicht erfasst | — |
| Type | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Generative protein binder design model | — |
| Intended use | Biological and pharmaceutical research | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Compatibility | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Limitations | Complex size affects memory needs; this record covers Chai-1 rather than later company models. | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Manufacturer | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Google DeepMind | — |
| Integration evidence | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | — |
| Laboratory handoff | Nicht erfasst | Nicht erfasst | Nicht erfasst | Nicht erfasst | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | — |