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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 | Protein Language Models | Protein Design | Protein Design | — |
| Company | University of Bayreuth | Pacesa Lab | Boltz Team | — |
| Access mode | Open Source | Research code or publication | Research code or publication | — |
| Licence conditions | Apache 2.0 | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | Standard GPU or multi-core CPU for inference | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | De novo protein sequence generation and enzyme design | Not recorded | Not recorded | — |
| Research workflow | Prompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed research (Nature Communications 2022) | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Open source release | Unknown, requires audit | Unknown, requires audit | — |
| Price | Free Open Source | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | Available on Hugging Face and GitHub | Public research method/code | Public research method/code | — |
| 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 | 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 | Generative Sequence Modeling | 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 | Autoregressive protein transformer | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | University of Bayreuth | Not recorded | Not recorded | — |
| Integration evidence | https://huggingface.co/nferruz/ProtGPT2 | Not recorded | Not recorded | — |
| Laboratory handoff | Synthesized sequences experimentally confirmed to adopt stable folds | Not recorded | Not recorded | — |