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.
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M.H.S.S. and M.P.W. thank the Deutsche Forschungsgemeinschaft (SFB858) for funding. M.H.S.S. and M.P.W. also thank D. Evans (RELX Intellectual Properties) and J. Swienty-Busch (Elsevier Information Systems) for the reaction dataset. We thank all AB-test participants in Shanghai and Münster, and J. Guo for assistance in AB testing. M.H.S.S. thanks M. Wiesenfeldt, the Studer group, D. Barton, S. McAnanama-Brereton, R. Vidyadharan and T. Kogej for discussions. M.P. thanks M. Winands and J. Togelius for insights.
The authors declare no competing financial interests.
Reviewer Information thanks D. Duvenaud, W. H. Green and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
The area under the curve is 0.99.
Extended Data Figure 2 An exemplary 10-step synthesis route for a complex intermediate in a drug synthesis.
It resembles the published route77 (with intermediates A and B) and was found by our algorithm autonomously within 30?s. The target was not contained in the training set.
Extended Data Figure 3 Example of reaction used in the AB testing, where the MCTS-derived route was less favoured.
In this task, the participants preferred the literature solution, as its key step was presumably perceived to be more convergent.
The output logit score of the neural network correlates surprisingly well with calculated quantum-mechanical properties (LUMO energies, in Hartree) in Diels–Alder reactions (r2?=?0.74) (a) and with empirically measured Hammond parameters in electrophilic brominations (r2?=?0.78) (b), even though the input features (ECFP4 fingerprints) do not contain electronic information.
This file contains the DOE, route diversity analysis, and failed molecules (including Supplementary Figures 1-7, Supplementary Table 1 and Supplementary References). Available on figshare (DOI 10.6084/m9.figshare.5832054) are 2 files, mcts_examples.pdf which contains routes found by the 3N-MCTS algorithm and heuristicBFS_examples.pdf which contains routes found by heuristic best first search without policy network and in-scope filter. (PDF 692 kb)
This file contains the AB test. (PDF 5648 kb)
This file contains the experiment for correlating the in-scope filter to physicochemical properties. (XLSX 45 kb)
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Segler, M., Preuss, M. & Waller, M. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018). https://doi.org/10.1038/nature25978
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