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Planning chemical syntheses with deep neural networks and symbolic AI

Author

Listed:
  • Marwin H. S. Segler

    (Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität
    BenevolentAI)

  • Mike Preuss

    (European Research Center for Information Systems)

  • Mark P. Waller

    (Shanghai University)

Abstract

To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.

Suggested Citation

  • Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
  • Handle: RePEc:nat:nature:v:555:y:2018:i:7698:d:10.1038_nature25978
    DOI: 10.1038/nature25978
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    Cited by:

    1. Hang Xiao & Rong Li & Xiaoyang Shi & Yan Chen & Liangliang Zhu & Xi Chen & Lei Wang, 2023. "An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Yuanyuan Jiang & Zongwei Yang & Jiali Guo & Hongzhen Li & Yijing Liu & Yanzhi Guo & Menglong Li & Xuemei Pu, 2021. "Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Nathan J. Szymanski & Pragnay Nevatia & Christopher J. Bartel & Yan Zeng & Gerbrand Ceder, 2023. "Autonomous and dynamic precursor selection for solid-state materials synthesis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Shuangjia Zheng & Tao Zeng & Chengtao Li & Binghong Chen & Connor W. Coley & Yuedong Yang & Ruibo Wu, 2022. "Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. de Mars, Patrick & O’Sullivan, Aidan, 2021. "Applying reinforcement learning and tree search to the unit commitment problem," Applied Energy, Elsevier, vol. 302(C).
    6. Debesh Mishra & Biswajit Mohapatra & Abhaya Sanatan Satpathy & Kamalakanta Muduli & Binayak Mishra & Swagatika Mishra & Upma Paliwal, 2024. "The pandemic COVID-19 and associated challenges with implementation of artificial intelligence (AI) in Indian agriculture," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2715-2729, June.
    7. Marcel Rolf Pfeifer, 2021. "Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
    8. Fernando Martinez-Plumed & Emilia Gomez Gutierrez & Jose Hernandez-Orallo, 2020. "AI Watch Assessing Technology Readiness Levels for Artificial Intelligence," JRC Research Reports JRC122014, Joint Research Centre.
    9. Naudé, Wim, 2020. "Artificial Intelligence against COVID-19: An Early Review," IZA Discussion Papers 13110, Institute of Labor Economics (IZA).
    10. Yu Wang & Chao Pang & Yuzhe Wang & Junru Jin & Jingjie Zhang & Xiangxiang Zeng & Ran Su & Quan Zou & Leyi Wei, 2023. "Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Weihe Zhong & Ziduo Yang & Calvin Yu-Chian Chen, 2023. "Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    12. Umit V. Ucak & Islambek Ashyrmamatov & Junsu Ko & Juyong Lee, 2022. "Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    13. Debesh Mishra & Kamalakanta Muduli & Rakesh Raut & Balkrishna Eknath Narkhede & Himanshu Shee & Sujoy Kumar Jana, 2023. "Challenges Facing Artificial Intelligence Adoption during COVID-19 Pandemic: An Investigation into the Agriculture and Agri-Food Supply Chain in India," Sustainability, MDPI, vol. 15(8), pages 1-25, April.
    14. Yuqiang Han & Xiaoyang Xu & Chang-Yu Hsieh & Keyan Ding & Hongxia Xu & Renjun Xu & Tingjun Hou & Qiang Zhang & Huajun Chen, 2024. "Retrosynthesis prediction with an iterative string editing model," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Itai Levin & Mengjie Liu & Christopher A. Voigt & Connor W. Coley, 2022. "Merging enzymatic and synthetic chemistry with computational synthesis planning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.
    17. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    18. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    19. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    20. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    21. Shingo Harada & Hiroki Takenaka & Tsubasa Ito & Haruki Kanda & Tetsuhiro Nemoto, 2024. "Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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