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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 Foundation Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Variant Effect Prediction | Protein Design | Protein Design | — |
| Company | Meta AI (FAIR) | Pacesa Lab | Boltz Team | — |
| Access mode | Open Source | Research code or publication | Research code or publication | — |
| Licence conditions | MIT License | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | 1x GPU (>= 16GB VRAM) for batch scoring | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Predicting viral escape, disease-associated mutations, and enzyme thermostability | Not recorded | Not recorded | — |
| Research workflow | Wild-type sequence + candidate mutations -> compute log-likelihood ratio scores | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed research (NeurIPS 2021) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Permissive open source release | Unknown, requires audit | Unknown, requires audit | — |
| Price | Free Open Source | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Available on GitHub and PyPI | Public research method/code | Public research method/code | — |
| Features | 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 | 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 | Variant Effect Prediction | Published biomedical AI method | Published biomedical AI method | — |
| Input data | Not recorded | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | Not recorded | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | — |
| Validation | Not recorded | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | — |
| Type | Masked protein language model ensemble | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | Meta AI | Not recorded | Not recorded | — |
| Integration evidence | https://github.com/facebookresearch/esm | Not recorded | Not recorded | — |
| Laboratory handoff | Guides directed evolution and deep mutational scanning library design | Not recorded | Not recorded | — |