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مقایسه شاخ‌به‌شاخ مدل‌های زیستی، موتورهای داکینگ و پایپ‌لاین‌های محاسباتی. فیلتر بر اساس مدل دسترسی، مصرف پردازشی و دسته‌بندی جهت اخذ تصمیم دقیق در کمتر از یک دقیقه.
| مشخصه | ＋ افزودن ابزار | |||||
|---|---|---|---|---|---|---|
| Category | Drug Discovery & Chemistry | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | AI Drug Discovery | — |
| Subcategory | Molecular Docking | Research AI Methods | Molecular Property Prediction | Molecular Design | Research AI Methods | — |
| Company | Scripps Research Institute | Research / method authors | Luo Lab | Sekijima Lab | Research / method authors | — |
| Access mode | Open Source | Confirm distribution and exact release | Research code or publication | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Apache 2.0 | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Multi-core CPU (GPU-accelerated version available via QuickVina/Vina-GPU) | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | Virtual screening of compound libraries and binding mode prediction | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Research workflow | Input receptor PDBQT and ligand PDBQT -> output binding poses and energy scores | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Evidence level | Peer-reviewed research with >25,000 scientific citations | PEER_REVIEWED_METHOD_OR_CONFERENCE | Peer-reviewed primary method | Peer-reviewed primary method | OFFICIAL_DOCUMENTATION | — |
| Commercial eligibility | Permissive open source release | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Verify access and licence | Check selected model licence | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | Available on GitHub and Scripps website | Method paper or official documentation | Public research method/code | Public research method/code | Method paper or official documentation | — |
| Features | High-speed multi-threaded molecular docking of flexible small-molecule ligands, Support for flexible receptor side chains and macrocyclic ligands, Python bindings (AutoDock-Vina Python package) for automated screening pipelines, Extensively integrated with open-source computational chemistry workflows | ADMET-AI scientific method, Molecular SMILES with well-defined compound stereochemistry and relevant assay domain, Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Chemical foundation model for learned molecular representations, property prediction and conditional molecule generation., Molecular features, predicted properties or generated SMILES | Diffusion-based ligand molecule generation constrained by specified pocket and pharmacophore interactions., Generated 3D chemical candidate structures | DiffSBDD scientific method, Protein binding pocket coordinates and desired molecular design context, Diffusion-generated candidate ligands | — |
| Entry type | Open Source Software | Source-scoped research method | Research AI model / method | Research AI model / method | Source-scoped research method | — |
| AI role | Empirical & Algorithmic Docking | ENSEMBLE_ML | Published biomedical AI method | Published biomedical AI method | DEEP_LEARNING | — |
| Input data | ثبت‌نشده | Molecular SMILES with well-defined compound stereochemistry and relevant assay domain | Valid SMILES; trained downstream endpoint or conditioned molecule task | Protein pocket coordinates; interaction constraints | Protein binding pocket coordinates and desired molecular design context | — |
| Output data | ثبت‌نشده | Predicted absorption, distribution, metabolism, excretion and toxicity endpoints | Molecular features; predicted properties or generated SMILES | Generated 3D chemical candidate structures | Diffusion-generated candidate ligands | — |
| Validation | ثبت‌نشده | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Molecular docking software | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Intended use | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Compatibility | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Manufacturer | The Scripps Research Institute | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Integration evidence | https://vina.scripps.edu | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |
| Laboratory handoff | Identified hits ordered from commercial vendors (Enamine, ChemBridge) for in vitro validation | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | ثبت‌نشده | — |