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A generalized-template-based graph neural network for accurate organic reactivity prediction

Nature Machine Intelligence volume 4, pages 772–780 (2022)Cite this article

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An Author Correction to this article was published on 15 November 2022

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Abstract

The reliable prediction of chemical reactivity remains in the realm of knowledgeable synthetic chemists. Automating this process by using artificial intelligence could accelerate synthesis design in future digital laboratories. While several machine learning approaches have demonstrated promising results, most current models deviate from how human chemists analyse and predict reactions based on electronic changes. Here, we propose a chemistry-motivated graph neural network called LocalTransform, which learns organic reactivity based on generalized reaction templates to describe the net changes in electron configuration between the reactants and products. The proposed concept dramatically reduces the number of reaction rules and exhibits state-of-the-art product prediction accuracy. In addition to the built-in interpretability of the generalized reaction templates, the high score–accuracy correlation of the model allows users to assess the uncertainty of the machine predictions.

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Fig. 1: The extraction process and examples of GRT.
Fig. 2: The overall prediction pipeline of LocalTransform.
Fig. 3: The top-1 exact match accuracy and the percentage of reactions as a function of prediction score.
Fig. 4: Six examples with high prediction score but ‘incorrect’ predictions by LocalTransform compared with the ground-truth product.
Fig. 5: Performance of LocalTransform on the human benchmark dataset.

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Data availability

The USPTO-480k dataset used in this manuscript is publicly available at https://github.com/wengong-jin/nips17-rexgen13. The source data used for each figure can be found at https://github.com/kaist-amsg/LocalTransform/releases/tag/raw_data40.

Code availability

The code for LocalTransform described in this manuscript is publicly available at https://github.com/kaist-amsg/LocalTransform40.

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References

  1. Engkvist, O. et al. Computational prediction of chemical reactions: current status and outlook. Drug Discov. Today 23, 1203–1218 (2018).

    Article  Google Scholar 

  2. de Almeida, A. F., Moreira, R. & Rodrigues, T. Synthetic organic chemistry driven by artificial intelligence. Nat. Rev. Chem. 3, 589–604 (2019).

    Article  Google Scholar 

  3. Struble, T. J. et al. Current and future roles of artificial intelligence in medicinal chemistry synthesis. J. Med. Chem. 63, 8667–8682 (2020).

    Article  Google Scholar 

  4. Jorner, K., Tomberg, A., Bauer, C., Sköld, C. & Norrby, P.-O. Organic reactivity from mechanism to machine learning. Nat. Rev. Chem. 5, 240–255 (2021).

    Article  Google Scholar 

  5. Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci. 2, 725–732 (2016).

    Article  Google Scholar 

  6. Segler, M. H. S. & Waller, M. P. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chem. Eur. J. 23, 5966–5971 (2017).

    Article  Google Scholar 

  7. Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H. & Jensen, K. F. Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci. 3, 434–443 (2017).

    Article  Google Scholar 

  8. Schwaller, P., Gaudin, T., Lányi, D., Bekas, C. & Laino, T. ‘Found in translation’: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem. Sci. 9, 6091–6098 (2018).

    Article  Google Scholar 

  9. Schwaller, P. et al. Molecular Transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572–1583 (2019).

    Article  Google Scholar 

  10. Tetko, I. V., Karpov, P., Van Deursen, R. & Godin, G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11, 5575 (2020).

    Article  Google Scholar 

  11. Irwin, R., Dimitriadis, S., He, J. & Bjerrum, E. Chemformer: a pre-trained transformer for computational chemistry. Mach. Learn.: Sci. Technol. 3, 015022 (2022).

    Google Scholar 

  12. Kayala, M. & Baldi, P. A. in Advances in Neural Information Processing Systems vol. 24 (NeurIPS, 2011).

  13. Jin, W., Coley, C., Barzilay, R. & Jaakkola, T. in Advances in Neural Information Processing Systems vol. 30 (NeurIPS, 2017).

  14. Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370–377 (2019).

    Article  Google Scholar 

  15. Bradshaw, J., Kusner, M. J., Paige, B., Segler, M. H. S. & Hernández-Lobato, J. M. A generative model for electron paths. In Int. Conf. for Learning Representations. (ICLR, 2019).

  16. Do, K., Tran, T. & Venkatesh, S. Graph Transformation Policy Network for Chemical Reaction Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 750–760 (ACM, 2019).

  17. Sacha, M. et al. Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits. J. Chem. Inf. Model. 61, 3273–3284 (2021).

    Article  Google Scholar 

  18. Qian, W. W. et al. Integrating deep neural networks and symbolic inference for organic reactivity prediction. Preprint at https://doi.org/10.26434/chemrxiv.11659563.v1 (2020).

  19. Bi, H. et al. Non-autoregressive electron redistribution modeling for reaction prediction. In Proceedings of the 38th International Conference on MachineLearning. (PMLR, 2021).

  20. Lowe, D. M. Extraction of chemical structures and reactions from the literature. Thesis, University of Cambridge (2012).

  21. Tu, Z. & Coley, C. W. Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction. J. Chem. Inf. Model. 62, 3503–3513 (2022).

    Article  Google Scholar 

  22. Li, M. et al. DGL-LifeSci: an open-source toolkit for deep learning on graphs in life science. ACS Omega 6, 27233–27238 (2021).

    Article  Google Scholar 

  23. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on MachineLearning (PMLR, 2017).

  24. Indigo toolkit. https://lifescience.opensource.epam.com/indigo/ EPAM [accessed 23 Aug 2022].

  25. Jaworski, W. et al. Automatic mapping of atoms across both simple and complex chemical reactions. Nat. Commun. 10, 1434 (2019).

    Article  Google Scholar 

  26. Schwaller, P., Hoover, B., Reymond, J.-L., Strobelt, H. & Laino, T. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci. Adv. 7, eabe4166 (2021).

    Article  Google Scholar 

  27. Toniato, A., Schwaller, P., Cardinale, A., Geluykens, J. & Laino, T. Unassisted noise reduction of chemical reaction datasets. Nat. Mach. Intell. 3, 485–494 (2021).

    Article  Google Scholar 

  28. Kearnes, S. M. et al. The Open Reaction Database. J. Am. Chem. Soc. 143, 18820–18826 (2021).

    Article  Google Scholar 

  29. Coley, C. W., Green, W. H. & Jensen, K. F. RDChiral: an RDKit wrapper for handling stereochemistry in retrosynthetic template extraction and application. J. Chem. Inf. Model. 59, 2529–2537 (2019).

    Article  Google Scholar 

  30. Pesciullesi, G., Schwaller, P., Laino, T. & Reymond, J.-L. Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates. Nat. Commun. 11, 4874 (2020).

    Article  Google Scholar 

  31. Pattanaik, L. et al. Message passing networks for molecules with tetrahedral chirality. Preprint at https://arxiv.org/abs/2012.00094 (2020).

  32. Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P. Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30, 595–608 (2016).

    Article  Google Scholar 

  33. Li, Y., Tarlow, D., Brockschmidt, M. & Zemel, R. Gated graph sequence neural networks. In Int. Conf. for Learning Representations (ICLR, 2016).

  34. Vaswani, A. et al. in Advances in Neural Information Processing Systems, pp 6000–6010 (NeurIPS, 2017).

  35. Shaw, P., Uszkoreit, J. & Vaswani, A. Self-attention with relative position representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT, 2018)

  36. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  Google Scholar 

  37. RDKit: Open-source cheminformatics; http://www.rdkit.org [accessed 23 Aug 2022].

  38. Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inform. Process. Syst. 32, 8026–8037 (2019).

    Google Scholar 

  39. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In 3rd Int. Conf. for Learning Representations (ICLR, 2017).

  40. Chen, S. kaist-amsg/LocalTransform: v1.0.0. Zenodo https://doi.org/10.5281/zenodo.6536406 (2022).

  41. Chen, S. & Jung, Y. Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au 1, 1612–1620 (2021).

    Article  Google Scholar 

  42. Yasuma, T. & Negoro, N. Condensed ring compound. US patent 7820837B2 (2010).

  43. Jensen, A. et al. Compounds. US patent 20080039450 (2008).

  44. Yamada, A. et al. N-coating heterocyclic compounds. US patent 20030176454 (2003).

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Acknowledgements

This work was supported by the Technology Innovation Program (20015850) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) and the National Research Foundation of Korea (2019M3D3A1A01069099, 2019M3D1A1079303, 2021R1A5A1030054).

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Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, KAIST, Daejeon, South Korea

    Shuan Chen & Yousung Jung

  2. Graduate School of AI, KAIST, Daejeon, South Korea

    Yousung Jung

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  1. Shuan Chen
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  2. Yousung Jung
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Contributions

S.C. and Y.J. conceived the project. S.C. designed the methods and performed the computational experiments and analyses. S.C. and Y.J. discussed the results and wrote the manuscript. Y.J. supervised the project.

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Correspondence to Yousung Jung.

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Supplementary Information

Supplementary Sections 1–8, containing Supplementary Figs. 1–9, Tables 1–4 and discussion.

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Chen, S., Jung, Y. A generalized-template-based graph neural network for accurate organic reactivity prediction. Nat Mach Intell 4, 772–780 (2022). https://doi.org/10.1038/s42256-022-00526-z

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