Loading BioAtlas...
Loading BioAtlas...
Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |||||
|---|---|---|---|---|---|---|
| Category | Biological AI Models | Biological AI Models | Biological AI Models | Biological AI Models | Biological Foundation Models | — |
| Subcategory | Protein Design | Protein Design | Protein Design | Protein Design | Variant Effect Prediction | — |
| Company | Pacesa Lab | Boltz Team | Baker Lab | Google DeepMind | Meta AI (FAIR) | — |
| Access mode | Research code or publication | Research code or publication | Research code or publication | Research Preview | Open Source | — |
| Licence conditions | Check code and model weights separately | Check code and model weights separately | Check code and model weights separately | Research Publication / DeepMind Research | MIT License | — |
| Compute requirements | 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 | 1x GPU (>= 16GB VRAM) for batch scoring | — |
| Biological application | Not recorded | Not recorded | Not recorded | De novo binder design, targeted cancer therapeutics, viral inhibitors, and biosensors | Predicting viral escape, disease-associated mutations, and enzyme thermostability | — |
| Research workflow | Not recorded | Not recorded | Not recorded | 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 | Peer-reviewed primary method | Primary research preprint | Primary research preprint | DeepMind Research Whitepaper & arXiv preprint (2024) | Peer-reviewed research (NeurIPS 2021) | — |
| Commercial eligibility | Unknown, requires audit | Unknown, requires audit | Unknown, requires audit | Research publication & early academic collaboration | Permissive open source release | — |
| Price | Check selected model licence | Check selected model licence | Check selected model licence | Research Preview | Free Open Source | — |
| Availability ⓘ | Public research method/code | Public research method/code | Public research method/code | DeepMind research publication and ongoing academic collaboration | Available on GitHub and PyPI | — |
| Features | 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 | 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 | Research AI model / method | Research AI model / method | Research AI model / method | AI Model | AI Model | — |
| AI role | Published biomedical AI method | Published biomedical AI method | Published biomedical AI method | De Novo Binder Design | Variant Effect Prediction | — |
| Input data | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | Target atomic geometry; designed region and constraints | Not recorded | Not recorded | — |
| Output data | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | Generated protein all-atom structures | Not recorded | Not recorded | — |
| Validation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Not recorded | Not recorded | — |
| Type | Not recorded | Not recorded | Not recorded | Generative protein binder design model | Masked protein language model ensemble | — |
| Intended use | Not recorded | Not recorded | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | Not recorded | Not recorded | Not recorded | Google DeepMind | Meta AI | — |
| Integration evidence | Not recorded | Not recorded | Not recorded | https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/ | https://github.com/facebookresearch/esm | — |
| Laboratory handoff | Not recorded | Not recorded | Not recorded | 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 | — |