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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 | Biomolecular Complex Prediction | — |
| Company | Baidu PaddleHelix | — |
| Access mode | Open Source & Web Service | — |
| Licence conditions | Apache 2.0 (code) / Model License | — |
| Compute requirements | 1x NVIDIA A100 or Kunlunxin AI accelerators | — |
| Biological application | Target-ligand interaction modeling, RNA-protein complex prediction, and covalent drug discovery | — |
| Research workflow | Input biomolecular sequence and SMILES -> generate 3D complex coordinates and confidence scores | — |
| Evidence level | Preprint and blind benchmark evaluation (2024) | — |
| Commercial eligibility | Open research access with enterprise private deployment | — |
| Price | Free Open Source / Enterprise Support | — |
| Availability ⓘ | Available globally on GitHub | — |
| Features | Unified prediction of protein-ligand, protein-nucleic acid, and multi-chain assemblies, High-throughput distributed inference accelerated by PaddleHelix engine, Includes specialized chemical bond loss for precise covalent drug interactions, Openly accessible web server and downloadable weights for enterprise deployment | — |
| Entry type | AI Model / Web Server | — |
| AI role | Structural Complex Modeling | — |
| Input data | Not recorded | — |
| Output data | Not recorded | — |
| Validation | Not recorded | — |
| Type | All-atom structure prediction model | — |
| Intended use | Not recorded | — |
| Compatibility | Not recorded | — |
| Manufacturer | Baidu Inc. | — |
| Integration evidence | https://github.com/PaddlePaddle/PaddleHelix | — |
| Laboratory handoff | Structures inform cryo-EM model fitting and crystallographic phasing | — |