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ColabReaction: Accelerating Transition State Searches with Machine Learning Potentials on Google Colaboratory
Open accessPublished version of a preprint
Abstract
We have developed a rapid and automated transition state (TS) search method for chemical reactions by combining the double-ended method, Direct MaxFlux (DMF), with machine learning (ML) potentials. Compared to conventional quantum mechanical (QM) scan-based approaches, this method achieves approximately 2 orders of magnitude speedup, typically locating TS structures within 10 min. To promote broad accessibility, this method is implemented on Google Colaboratory (Colab), leveraging its cloud-based GPU environment to eliminate the need for local computational resources. We named this implementation as ColabReaction. A modified panel-based graphical user interface is also provided, allowing users to perform TS searches through a web-based interface without writing code. This platform offers a cost-free, user-friendly solution for reaction pathway exploration and mechanistic analysis, particularly for experimental researchers and students without prior experience in computational chemistry. ColabReaction is open-source and freely available at https://ColabReaction.net and https://github.com/BILAB/ColabReaction.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
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Research works printed but not linked: 34
- 10.1021/acs.orglett.4c04346 · DOI
- 10.1021/jacs.2c09308 · DOI
- 10.1039/D4SC01381F · DOI
- 10.1063/1.2841941 · DOI
- 10.1063/1.451695 · DOI
- 10.1063/1.1329672 · DOI
- 10.1021/jp0455430 · DOI
- 10.1063/1.1691018 · DOI
- 10.1021/acs.jctc.3c01246 · DOI
- 10.1021/acs.jctc.4c01549 · DOI
- 10.1021/acs.jctc.4c00792 · DOI
- 10.1021/acs.jcim.4c01714 · DOI
- 10.1021/jacs.4c16135 · DOI
- Wood, B. M.; Dzamba, M.; Fu, X.; Gao, M.; Shuaibi, M.; Barroso-Luque, L.; Abdelmaqsoud, K.; Gharakhanyan, V.; Kitchin, J. R.; Levine, D. S.UMA: A Family of Universal Models for Atoms. arXiv preprint , 2025, https://ai.meta.com/research/publications/uma-a-family-of-universal-models-for-atoms (accessed July 14th, 2025).
- Levine, D. S.; Shuaibi, M.; Spotte-Smith, E. W. C.; Taylor, M. G.; Hasyim, M. R.; Michel, K.; Batatia, I.; Csányi, G.; Dzamba, M.; Eastman, P.; Frey, N. C.; Fu, X.; Gharakhanyan, V.; Krishnapriyan, A. S.; Rackers, J. A.; Raja, S.; Rizvi, A.; Rosen, A. S.; Ulissi, Z.; Vargas, S.; Zitnick, C. L.; Blau, S. M.; Wood, B. M. The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models. arXiv [physics.chemph] , 2025, 10.48550/arXiv.2505.08762. · DOI
- 10.26434/chemrxiv-2025-md8k6-v2 · DOI
- 10.1101/2025.06.14.659707 · DOI
- 10.1039/C6SC05720A · DOI · Paper page
- 10.1021/acs.jpcc.4c01704 · DOI
- 10.1002/jcc.20290 · DOI · Paper page
- 10.1016/j.softx.2015.06.001 · DOI · Paper page
- 10.1038/s41592-022-01488-1 · DOI · Paper page
- 10.1093/bioinformatics/bty1057 · DOI · Paper page
- 10.1038/nbt.3988 · DOI · Paper page
- 10.1126/science.abj8754 · DOI · Paper page
- 10.1007/978-1-4842-4470-8_7 · DOI
- 10.1021/ct400319w · DOI
- 10.1002/jcc.23910 · DOI
- 10.1021/acs.jctc.1c00462 · DOI
- 10.1021/jacs.0c09616 · DOI
- 10.1021/acscatal.2c01345 · DOI
- 10.1021/cr900121s · DOI
- 10.1039/D3OB00728F · DOI
- 10.1002/chem.202203055 · DOI
Metadata
- Authors
- Karasawa, M., Leow, C. S., Yajima, H., Arai, S., Nishizaki, H., Terada, T., & Sato, H.
- Affiliations
- The University of Tokyo (JP)
- University of Yamanashi (JP)
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo (JP)
- Corresponding author
- Terada, Tohru
- Sato, Hajime
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-10-28
- Volume
- 65
- Issue
- 21
- Pages
- 11908-11914
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4415626296
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 8 · 2025: 1
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Japan Agency for Medical Research and Development (JP25ama121027)
- Japan Society for the Promotion of Science (22H05125, 22H05126, 24K01626, 25H01577)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- transition state search, machine learning interatomic potentials, reaction mechanism, Google Colaboratory, GPU computing, web-based graphical user interface
- MeSH terms
- Machine Learning, Software, User-Computer Interface, Internet
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