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End-to-end computational pipeline for structural biology, macrocycles, and targeted biologics.
Evaluate, benchmark, and deploy modern generative diffusion and foundation protein models. Compare AlphaFold 3, RFdiffusion, Chai-1, and ProteinMPNN across licensing, GPU hardware constraints, and experimental validation evidence.
The previous fixed-template workflow scorer cannot establish comparative scientific suitability. For a task-specific assessment, enter the biological objective, the actual sample/data modalities and method constraints in the offline Decision Lab. Select the reviewed method deliberately and compare it against a mandatory non-AI baseline.
Open scientific Decision LabHistorical workflow templates are examples, not validated executable pipelines, clinical decisions or ranked model recommendations.
| Herramienta y organización | Licencia y términos | GPU / Hardware | Arquitectura | Formato de entrada | Nivel de evidencia | Catálogo |
|---|---|---|---|---|---|---|
| AlphaFold 3 Google DeepMind & Isomorphic Labs | Non-Commercial / Web Server API | A100 80GB (Local) / Cloud Server | Open Code / Model Weights Gated | FASTA sequences + Ligand mmCIF/SMILES | Peer-reviewed (Nature 2024) | Ver herramienta → |
| RFdiffusion Institute for Protein Design (Baker Lab) | BSD 3-Clause (Open) | 1x RTX 4090 / A100 | Open Source / Open Weights | Target PDB + Hotspot residues / Motifs | Peer-reviewed (Nature 2023) | Ver herramienta → |
| ProteinMPNN Dauparas et al. / IPD | MIT (Open Source) | CPU or modest GPU (T4 / RTX 3080) | Open Source / Open Weights | PDB backbone 3D coordinates | Peer-reviewed (Science 2022) | Ver herramienta → |
| Chai-1 Chai Discovery | Chai Academic & Commercial Terms | A100 80GB / Cloud API | Open Model Weights / Web Platform | FASTA + SMILES + Restraints | Preprint (bioRxiv 2024) | Ver herramienta → |
| RoseTTAFold All-Atom Baker Lab / University of Washington | BSD 3-Clause | 1x A100 80GB | Open Source | Protein, Nucleic Acid & Chemical SMILES | Peer-reviewed (Science 2024) | Ver herramienta → |
| ESMFold Meta AI / EvolutionaryScale | MIT (Open Source) | NVIDIA V100 / A100 (Ultra-fast inference) | Open Source / Open Weights | Single FASTA sequence (No MSA needed) | Peer-reviewed (Science 2023) | Ver herramienta → |
| ColabFold Mirdita et al. | MIT (Open Source) | Free Google Colab T4 / Local GPU | Open Source | FASTA sequence / MMseqs2 query | Peer-reviewed (Nature Methods 2022) | Ver herramienta → |
Elija la arquitectura operativa adecuada a su infraestructura de cómputo, restricciones de presupuesto y libertad de licencias.
Optimized for grant budgets and academic compute clusters without vendor lock-in or recurring cloud fees.
Generate baseline target receptor coordinates and evaluate per-residue pLDDT confidence scores.
Generate 1,000–5,000 candidate binder backbones around defined target interface hotspots.
Design 3–8 sequence variations per candidate backbone with high predicted soluble expression.
Filter complexes by monomer pLDDT > 80, complex pTM > 0.7, and interface RMSD < 2.0 Å before gene synthesis.
Engineered for venture-backed drug discovery startups requiring speed, IP clarity, and multimodal co-folding.
Run multimodal co-folding with experimental cross-linking and epitope restraints under commercial licensing.
Deploy parallelized RFdiffusion containers across AWS/GCP spot GPU instances for rapid binder generation.
Simultaneously pack sidechains in the presence of non-protein cofactors, ions, and prosthetic groups.
Automate plate map generation and direct export to Twist / IDT for high-throughput yeast display screening.
Examine recuentos de parámetros detallados, puntuaciones de referencia, límites de entrada y restricciones de licencia en la tabla de comparación interactiva de BioAtlas.
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