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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 | Protein Language Models | — |
| Company | University of Bayreuth | — |
| Access mode | Open Source | — |
| Licence conditions | Apache 2.0 | — |
| Compute requirements | Standard GPU or multi-core CPU for inference | — |
| Biological application | De novo protein sequence generation and enzyme design | — |
| Research workflow | Prompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold | — |
| Evidence level | Peer-reviewed research (Nature Communications 2022) | — |
| Commercial eligibility | Open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on Hugging Face and GitHub | — |
| Features | Autoregressive unconditional and conditional sequence generation, Produces sequences with natural secondary structure propensities and globular folding, Trained on UniRef50 with zero structural supervision required, Available on Hugging Face Model Hub with straightforward pipeline inference | — |
| Entry type | AI Model | — |
| AI role | Generative Sequence Modeling | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | Autoregressive protein transformer | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | University of Bayreuth | — |
| Integration evidence | https://huggingface.co/nferruz/ProtGPT2 | — |
| Laboratory handoff | Synthesized sequences experimentally confirmed to adopt stable folds | — |