The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction
Open accessAbstract
Accurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule–protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000 × more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code 1 under a permissive open license, providing a robust and extensible foundation for both academic and industrial research.
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Cites
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840
MMPBSA.py: An Efficient Program for End-State Free Energy Calculations
Miller, B. R., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300418h · Cited by 4,609
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,944
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac956 · Cited by 3,443
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,124
Case, D. A., Aktulga, H. M., Belfon, K., Cerutti, D. S., Cisneros, G. A., Cruzeiro, V. W. D., Forouzesh, N., Giese, T. J., Götz, A. W., Gohlke, H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01153 · Cited by 2,089
Cited by
Deep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66
Atomic resolution ensembles of intrinsically disordered proteins with Alphafold
Schnapka, V., Morozova, T. I., Sen, S., & Bonomi, M. (2026) · Nature Communications · doi:10.1038/s41467-026-69172-y · Cited by 23
Evaluating generalization in protein–ligand cofolding methods
Škrinjar, P., Eberhardt, J., Studer, G., Tauriello, G., Schwede, T., & Durairaj, J. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01797-5 · Cited by 22
Koyama, T., Tsumura, H., Okita, R., Yamazaki, K., Hasegawa, A., Imamura, K., Kato, T., Iwata, H., Kojima, R., Inoue, H., et al. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01155-z · Cited by 9
Karasawa, M., Leow, C. S., Yajima, H., Arai, S., Nishizaki, H., Terada, T., & Sato, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02398 · Cited by 9
Metadata
- Authors
- 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.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2025-06-18
- OpenAlex
- W4411463597
- Cited by
- 665
- References
- 56
- Type
- posted content
- Language
- en
- Open access
- green
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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. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.06.14.659707
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