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Confronta modelli biologici, motori di docking e flussi di calcolo affiancati. Filtra per modello di accesso, impronta di calcolo e categoria per ottenere una tabella decisionale attuabile in meno di un minuto.
| Attributo | ＋ Aggiungi strumento | |||
|---|---|---|---|---|
| Category | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Design | Protein Design | Protein Design | — |
| Company | Google DeepMind | Pacesa Lab | Boltz Team | — |
| Access mode | Research Preview | Research code or publication | Research code or publication | — |
| Licence conditions | Research Publication / DeepMind Research | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | GPU cluster for generative diffusion sampling and candidate filtering | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | Non registrato | Non registrato | — |
| Research workflow | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | Non registrato | Non registrato | — |
| Evidence level | DeepMind Research Whitepaper & arXiv preprint (2024) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Research publication & early academic collaboration | Unknown, requires audit | Unknown, requires audit | — |
| Price | Research Preview | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | DeepMind research publication and ongoing academic collaboration | Public research method/code | Public research method/code | — |
| Features | 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 | 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 | Research AI model / method | Research AI model / method | — |
| AI role | De Novo Binder Design | Published biomedical AI method | Published biomedical AI method | — |
| Input data | Non registrato | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | Non registrato | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | — |
| Validation | Non registrato | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | — |
| Type | Generative protein binder design model | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Manufacturer | Google DeepMind | Non registrato | Non registrato | — |
| Integration evidence | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | Non registrato | Non registrato | — |
| Laboratory handoff | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | Non registrato | Non registrato | — |