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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 | — |
| Subcategory | Variant Effect Prediction | — |
| Company | Meta AI (FAIR) | — |
| Access mode | Open Source | — |
| Licence conditions | MIT License | — |
| Compute requirements | 1x GPU (>= 16GB VRAM) for batch scoring | — |
| Biological application | Predicting viral escape, disease-associated mutations, and enzyme thermostability | — |
| Research workflow | Wild-type sequence + candidate mutations -> compute log-likelihood ratio scores | — |
| Evidence level | Peer-reviewed research (NeurIPS 2021) | — |
| Commercial eligibility | Permissive open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on GitHub and PyPI | — |
| 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 | — |
| Entry type | AI Model | — |
| AI role | Variant Effect Prediction | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | Masked protein language model ensemble | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | Meta AI | — |
| Integration evidence | https://github.com/facebookresearch/esm | — |
| Laboratory handoff | Guides directed evolution and deep mutational scanning library design | — |