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Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Nature Computational Science volume 3, pages 1045–1055 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures—reactant, transition state and product—in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol–1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

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Fig. 1: Overview of equivariant diffusion models (EDMs) for generative molecular system sampling.
Fig. 2: Object-aware SE(3) equivariance and its implementation based on SE(3)-equivariant GNNs.
Fig. 3: Analysis of samples generated by OA-ReactDiff on select elementary reactions.
Fig. 4: Evaluation of structural similarities for TS structures generated by OA-ReactDiff and true TS structures.
Fig. 5: Energetic performance for OA-ReactDiff plus recommender TS structures.

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Data availability

The Transition1x dataset55 used in this work can be found at GitLab, https://gitlab.com/matschreiner/Transition1x, with https://doi.org/10.6084/m9.figshare.19614657.v4. Source data are provided with this paper.

Code availability

Codebase for OA-ReactDiff is available as an open-source repository on GitHub for contiguous development, https://github.com/chenruduan/OAReactDiff. A stable version of the code56 used in this work is available at Zenodo, https://doi.org/10.5281/zenodo.10054963.

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Acknowledgements

This work was supported by the US Office of Naval Research under grant no. N00014-20-1-2150 (C.D. and H.J.K.) and National Science Foundation grant CBET-1846426 (H.J. and H.J.K.). C.D. thanks the Molecular Sciences Software Institute for the fellowship support under NSF grant OAC-1547580. C.D. thanks Q. Zhao and M. Monkey for discussions about elementary reactions. C.D. thanks A. Nandy and W. Du for discussions about equivariant graph neural networks. C.D. and Y.D. thank G.-H. Liu and T. Chen for discussions about diffusion model and Schrödinger bridge. C.D. and H.J. thank Y. Zhao for his help on preparing a demo Jupyter notebook for this work. The authors thank S. Choi and M. Schreiner for communications and providing their raw data that makes the comparison in Table 1 possible.

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Authors and Affiliations

  1. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, US

    Chenru Duan, Haojun Jia & Heather J. Kulik

  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, US

    Chenru Duan, Haojun Jia & Heather J. Kulik

  3. Department of Computer Science, Cornell University, Ithaca, NY, US

    Yuanqi Du

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Contributions

C.D. was responsible for conceptualization, methodology, software, validation, investigation, data curation, writing of the original draft, review, editing and visualization. Y.D. was responsible for methodology, software, writing of original draft, review and editing. H.J. was responsible for data curation, review and editing. H.J.K. was responsible for writing of the original draft, review and editing.

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Correspondence to Chenru Duan.

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Nature Computational Science thanks Sunghwan Choi, Hyunwook Jung and Matteo Maestri for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Duan, C., Du, Y., Jia, H. et al. Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nat Comput Sci 3, 1045–1055 (2023). https://doi.org/10.1038/s43588-023-00563-7

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