NextIn Bio / Model Book
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Put the practical questions next to the science. These are existing editorial records, not a recommendation or a universal ranking. Check the original sources before making a decision.
| Decision lens | AlphaFold3 ↗Google DeepMind | Boltz-1 ↗MIT |
|---|---|---|
| Capabilities | Structure Prediction · Complex Prediction · Ligand Docking | Structure Prediction · Complex Prediction |
| Evidence | CASP15: 0.85 GDT-TS (avg) — Dominant across all categories POSEBUSTERS: 77% valid — Best-in-class for ligand pose validity DOCKQ: 0.73 DockQ — Protein-protein complex prediction | CASP15: 0.82 GDT-TS (avg) — Competitive with AF3 on single chains DOCKQ: 0.68 DockQ — Complex prediction close to AF3 |
| Licensing | Free for academic use via API. Commercial use requires licensing from Google DeepMind. No weight distribution. | MIT license. Fully open weights, code, and training pipeline. |
| Deployment | API-only — no local deployment possible Free via AlphaFold Server (rate-limited), enterprise pricing undisclosed | 1x A100 40GB for most predictions Self-hosted only — ~$0.02/prediction on cloud GPU |
| Tradeoffs | Production drug discovery pipelines requiring IP freedom; High-throughput screening where API rate limits are a bottleneck; Regulated workflows requiring on-premises deployment; Any application where you need to fine-tune or modify the model | When absolute peak accuracy on complexes matters more than accessibility; Ligand docking — use Boltz-2 or DiffDock instead |
| Sources & review | AlphaFold3 Paper (Nature 2024) ↗AlphaFold Server ↗CASP15 Results ↗Record reviewed | Boltz-1 GitHub ↗Record reviewed |