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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 AI Models | Biological Foundation Models | — |
| Subcategory | Protein Design | Variant Effect Prediction | — |
| Company | Google DeepMind | Meta AI (FAIR) | — |
| Access mode | Research Preview | Open Source | — |
| Licence conditions | Research Publication / DeepMind Research | MIT License | — |
| Compute requirements | GPU cluster for generative diffusion sampling and candidate filtering | 1x GPU (>= 16GB VRAM) for batch scoring | — |
| Biological application | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | Predicting viral escape, disease-associated mutations, and enzyme thermostability | — |
| Research workflow | Specify target protein structure and epitope binding site -> generate de novo binder sequences and 3D backbones | Wild-type sequence + candidate mutations -> compute log-likelihood ratio scores | — |
| Evidence level | DeepMind Research Whitepaper & arXiv preprint (2024) | Peer-reviewed research (NeurIPS 2021) | — |
| Commercial eligibility | Research publication & early academic collaboration | Permissive open source release | — |
| Price | Research Preview | Free Open Source | — |
| Availability ⓘ | DeepMind research publication and ongoing academic collaboration | Available on GitHub and PyPI | — |
| 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 | State-of-the-art zero-shot variant effect prediction across 41 deep mutational scanning datasets, Ensemble of five distinct models trained on UniRef90 with different random seeds, Captures epistatic and structural constraints directly from evolutionary sequences, Standard benchmark baseline in computational biology for mutation scoring | — |
| Entry type | AI Model | AI Model | — |
| AI role | De Novo Binder Design | Variant Effect Prediction | — |
| Input data | Non enregistré | Non enregistré | — |
| Output data | Non enregistré | Non enregistré | — |
| Validation | Non enregistré | Non enregistré | — |
| Type | Generative protein binder design model | Masked protein language model ensemble | — |
| Intended use | Non enregistré | Non enregistré | — |
| Compatibility | Non enregistré | Non enregistré | — |
| Manufacturer | Google DeepMind | Meta AI | — |
| Integration evidence | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | https://github.com/facebookresearch/esm | — |
| Laboratory handoff | Designed binder sequences are ordered as synthetic DNA for yeast surface display and SPR/BLI affinity validation | Guides directed evolution and deep mutational scanning library design | — |