Boltz-2 (AlphaFold3)
Open-source AlphaFold3-class model (Boltz-2) with built-in affinity prediction.
Overview
Boltz-2 is the next-generation open-source deep-learning model family that reaches AlphaFold3-level accuracy for 3-D biomolecular complex prediction and adds a dedicated head for near-FEP binding-affinity estimation. Architectural depth, physics-aware Boltz-steering potentials, ensemble-based training, and fine-grained controllability together deliver fast, clash-free structures and quantitative interaction scores across proteins, nucleic acids, and small molecules. Released under the permissive MIT license, Boltz-2 ships with full training code, pretrained weights, and curated datasets to accelerate discovery in drug design and structural biology.
Run Boltz-2 (AlphaFold3) on Neurosnap
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Features
- Dedicated affinity-prediction head with near-FEP accuracy for binding-energy estimation.
- Ensemble-based training on NMR and MD trajectories to capture conformational dynamics.
- Fine-grained controllability via experimental-method, template, and pocket/distance constraints.
- Boltz-steering physics potentials yield clash-free, stereochemically correct structures.
- Deeper mixed-precision PairFormer (64 layers) with larger crops for faster inference on large complexes.
- Extended input representation (cyclic/modified flags, bond-order, symmetric positional encodings).
- Millions of curated biochemical-assay measurements for reliable affinity and generalization.
- Random MSA dropout and ensemble supervision boost single-sequence robustness.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access Boltz-2 (AlphaFold3) using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using Boltz-2 (AlphaFold3) in publications or research outputs.
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Passaro, S., Corso, G., Wohlwend, J., Reveiz, M., Thaler, S., Somnath, V. R., Getz, N., Portnoi, T., Roy, J., Stark, H., Kwabi-Addo, D., Beaini, D., Jaakkola, T., & Barzilay, R. (2025). Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. |
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Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nature Methods. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |