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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |
|---|---|---|
| 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 | 未明确记录 | — |
| Output data | 未明确记录 | — |
| Validation | 未明确记录 | — |
| Type | Masked protein language model ensemble | — |
| Intended use | 未明确记录 | — |
| Compatibility | 未明确记录 | — |
| Manufacturer | Meta AI | — |
| Integration evidence | https://github.com/facebookresearch/esm | — |
| Laboratory handoff | Guides directed evolution and deep mutational scanning library design | — |