Synthetic organic chemistry driven by artificial intelligence

Synthetic organic chemistry underpins several areas of chemistry, including drug discovery, chemical biology, materials science and engineering. However, the execution of complex chemical syntheses in itself requires expert knowledge, usually acquired over many years of study and hands-on laboratory practice. The development of technologies with potential to streamline and automate chemical synthesis is a half-century-old endeavour yet to be fulfilled. Renewed interest in artificial intelligence (AI), driven by improved computing power, data availability and algorithms, is overturning the limited success previously obtained. In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature. By examining the underlying concepts, we aim to demystify AI for bench chemists in order that they may embrace it as a tool rather than fear it as a competitor, spur future research by pinpointing the gaps in knowledge and delineate how chemical AI will run in the era of digital chemistry.

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References

  1. Nantermet, P. G. Reaction: the art of synthetic chemistry. Chem1, 335–336 (2016). CASGoogle Scholar
  2. Nicolaou, K. C. & Chen, J. S. The art of total synthesis through cascade reactions. Chem. Soc. Rev.38, 2993–3009 (2009). CASPubMedPubMed CentralGoogle Scholar
  3. Baran, P. S. Natural product total synthesis: as exciting as ever and here to stay. J. Am. Chem. Soc.140, 4751–4755 (2018). CASPubMedGoogle Scholar
  4. Ley, S. V. The engineering of chemical synthesis: humans and machines working in harmony. Angew. Chem. Int. Ed.57, 5182–5183 (2018). CASGoogle Scholar
  5. Bergman, R. G. & Danheiser, R. L. Reproducibility in chemical research. Angew. Chem. Int. Ed.55, 12548–12549 (2016). CASGoogle Scholar
  6. Duros, V. et al. Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew. Chem. Int. Ed.56, 10815–10820 (2017). CASGoogle Scholar
  7. Roch, L. M. et al. ChemOS: Orchestrating autonomous experimentation. Science Robot.3, eaat5559 (2018). Google Scholar
  8. Schneider, G. Mind and machine in drug design. Nat. Mach. Intell.1, 128–130 (2019). Google Scholar
  9. Wang, Y. et al. Acoustic droplet ejection enabled automated reaction scouting. ACS Cent. Sci.5, 451–457 (2019). CASPubMedPubMed CentralGoogle Scholar
  10. Fitzpatrick, D. E., Battilocchio, C. & Ley, S. V. Enabling technologies for the future of chemical synthesis. ACS Cent. Sci.2, 131–138 (2016). CASPubMedPubMed CentralGoogle Scholar
  11. Ley, S. V., Fitzpatrick, D. E., Myers, R. M., Battilocchio, C. & Ingham, R. J. Machine-assisted organic synthesis. Angew. Chem. Int. Ed.54, 10122–10136 (2015). CASGoogle Scholar
  12. Lehmann, J. W., Blair, D. J. & Burke, M. D. Toward generalization of iterative small molecule synthesis. Nat. Rev. Chem.2, 0115 (2018). PubMedPubMed CentralGoogle Scholar
  13. Corey, E. J. & Wipke, W. T. Computer-assisted design of complex organic syntheses. Science166, 178–192 (1969). CASPubMedGoogle Scholar
  14. Pensak, D. A. & Corey, E. J. in Computer-Assisted Organic Synthesis Ch. 1 (eds Wipke, W. T. & Howe, W. J.) 1-32 (American Chemical Society, 1977).
  15. Lajiness, M. S., Maggiora, G. M. & Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem.47, 4891–4896 (2004). CASPubMedGoogle Scholar
  16. Earkin, D. R. & Warr, W. A. in Computer-Assisted Organic Synthesis Ch. 10 (eds Wipke, W. T. & Howe, W. J.) 217-226 (American Chemical Society, 1977).
  17. Sridharan, N. S. in Computer-Assisted Organic Synthesis Ch. 7 (eds Wipke, W. T. & Howe, W. J.) 148-178 (American Chemical Society, 1977).
  18. Wipke, W. T., Ouchi, G. I. & Krishnan, S. Simulation and evaluation of chemical synthesis—SECS: An application of artificial intelligence techniques. Artif. Intell.11, 173–193 (1978). Google Scholar
  19. Hessler, G. & Baringhaus, K. H. Artificial intelligence in drug design. Molecules23, E2520 (2018). PubMedGoogle Scholar
  20. Sellwood, M. A., Ahmed, M., Segler, M. H. & Brown, N. Artificial intelligence in drug discovery. Future Med. Chem.10, 2025–2028 (2018). CASPubMedGoogle Scholar
  21. Aspuru-Guzik, A., Lindh, R. & Reiher, M. The matter simulation (r)evolution. ACS Cent. Sci.4, 144–152 (2018). CASPubMedPubMed CentralGoogle Scholar
  22. Lusher, S. J., McGuire, R., van Schaik, R. C., Nicholson, C. D. & de Vlieg, J. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today19, 859–868 (2014). CASPubMedGoogle Scholar
  23. Tetko, I. V., Engkvist, O., Koch, U., Reymond, J. L. & Chen, H. BIGCHEM: challenges and opportunities for big data analysis in chemistry. Mol. Inf.35, 615–621 (2016). CASGoogle Scholar
  24. Henson, A. B., Gromski, P. S. & Cronin, L. Designing algorithms to aid discovery by chemical robots. ACS Cent. Sci.4, 793–804 (2018). CASPubMedPubMed CentralGoogle Scholar
  25. Rich, A. S. & Gureckis, T. M. Lessons for artificial intelligence from the study of natural stupidity. Nat. Mach. Intell.1, 174–180 (2019). Google Scholar
  26. Ekins, S. et al. Exploiting machine learning for end-to-end drug discovery and development. Nat. Mater.18, 435–441 (2019). CASPubMedPubMed CentralGoogle Scholar
  27. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res.46, D1074–D1082 (2018). CASPubMedGoogle Scholar
  28. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res.45, D945–D954 (2017). CASPubMedGoogle Scholar
  29. Kim, S. et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res.47, D1102–D1109 (2019). PubMedGoogle Scholar
  30. Grzybowski, B. A. et al. Chematica: A story of computer code that started to think like a chemist. Chem4, 390–398 (2018). CASGoogle Scholar
  31. Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature555, 604–610 (2018). CASPubMedGoogle Scholar
  32. Schneider, N., Lowe, D. M., Sayle, R. A., Tarselli, M. A. & Landrum, G. A. Big data from pharmaceutical patents: A computational analysis of medicinal chemists’ bread and butter. J. Med. Chem.59, 4385–4402 (2016). CASPubMedGoogle Scholar
  33. Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross-coupling using machine learning. Science360, 186–190 (2018). CASPubMedGoogle Scholar
  34. Roughley, S. D. & Jordan, A. M. The medicinal chemist’s toolbox: an analysis of reactions used in the pursuit of drug candidates. J. Med. Chem.54, 3451–3479 (2011). CASPubMedGoogle Scholar
  35. Lowe, D. AI designs organic syntheses. Nature555, 592–593 (2018). CASPubMedGoogle Scholar
  36. Coley, C. W., Green, W. H. & Jensen, K. F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res.51, 1281–1289 (2018). CASPubMedGoogle Scholar
  37. Gelernter, H. L. et al. Empirical explorations of SYNCHEM. Science197, 1041–1049 (1977). CASPubMedGoogle Scholar
  38. Cadeddu, A., Wylie, E. K., Jurczak, J., Wampler-Doty, M. & Grzybowski, B. A. Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses. Angew. Chem. Int. Ed.53, 8108–8112 (2014). CASGoogle Scholar
  39. Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci.3, 1237–1245 (2017). CASPubMedPubMed CentralGoogle Scholar
  40. Hartenfeller, M. et al. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput. Biol.8, e1002380 (2012). CASPubMedPubMed CentralGoogle Scholar
  41. Rodrigues, T. et al. De novo design and optimization of Aurora A kinase inhibitors. Chem. Sci.4, 1229–1233 (2013). CASGoogle Scholar
  42. Rodrigues, T. et al. Steering target selectivity and potency by fragment-based de novo drug design. Angew. Chem. Int. Ed.52, 10006–10009 (2013). CASGoogle Scholar
  43. Friedrich, L., Rodrigues, T., Neuhaus, C. S., Schneider, P. & Schneider, G. From complex natural products to simple synthetic mimetics by computational de novo design. Angew. Chem. Int. Ed.55, 6789–6792 (2016). CASGoogle Scholar
  44. Lewell, X. Q., Judd, D. B., Watson, S. P. & Hann, M. M. RECAP — retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J. Chem. Inf. Comput. Sci.38, 511–522 (1998). CASPubMedGoogle Scholar
  45. Reker, D., Bernardes, G. J. L. & Rodrigues, T. Computational advances in combating colloidal aggregation in drug discovery. Nat. Chem.11, 402–418 (2019). CASPubMedGoogle Scholar
  46. Liu, B. et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci.3, 1103–1113 (2017). CASPubMedPubMed CentralGoogle Scholar
  47. Altae-Tran, H., Ramsundar, B., Pappu, A. S. & Pande, V. Low data drug discovery with one-shot learning. ACS Cent. Sci.3, 283–293 (2017). CASPubMedPubMed CentralGoogle Scholar
  48. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today23, 1241–1250 (2018). PubMedGoogle Scholar
  49. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface15, 20170387 (2018). PubMedPubMed CentralGoogle Scholar
  50. Baylon, J. L., Cilfone, N. A., Gulcher, J. R. & Chittenden, T. W. Enhancing retrosynthetic reaction prediction with deep learning using multiscale reaction classification. J. Chem. Inf. Model.59, 673–688 (2019). CASPubMedGoogle Scholar
  51. Fialkowski, M., Bishop, K. J., Chubukov, V. A., Campbell, C. J. & Grzybowski, B. A. Architecture and evolution of organic chemistry. Angew. Chem. Int. Ed.44, 7263–7269 (2005). CASGoogle Scholar
  52. Gothard, C. M. et al. Rewiring chemistry: algorithmic discovery and experimental validation of one-pot reactions in the network of organic chemistry. Angew. Chem. Int. Ed.51, 7922–7927 (2012). CASGoogle Scholar
  53. Grzybowski, B. A., Bishop, K. J., Kowalczyk, B. & Wilmer, C. E. The ‘wired’ universe of organic chemistry. Nat. Chem.1, 31–36 (2009). CASPubMedGoogle Scholar
  54. Kowalik, M. et al. Parallel optimization of synthetic pathways within the network of organic chemistry. Angew. Chem. Int. Ed.51, 7928–7932 (2012). CASGoogle Scholar
  55. Segler, M. H. S. & Waller, M. P. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chem. Eur. J.23, 5966–5971 (2017). CASPubMedGoogle Scholar
  56. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature529, 484–489 (2016). CASPubMedGoogle Scholar
  57. Browne, C. et al. A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games4, 1–43 (2012). Google Scholar
  58. Schreck, J. S., Coley, C. W. & Bishop, K. J. M. Learning retrosynthetic planning through simulated experience. ACS Cent. Sci.5, 970–981 (2019). CASPubMedPubMed CentralGoogle Scholar
  59. Szymkuc, S. et al. Computer-assisted synthetic planning: The end of the beginning. Angew. Chem. Int. Ed.55, 5904–5937 (2016). CASGoogle Scholar
  60. Klucznik, T. et al. Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem4, 522–532 (2018). CASGoogle Scholar
  61. Molga, K., Dittwald, P. & Grzybowski, B. A. Navigating around patented routes by preserving specific motifs along computer-planned retrosynthetic pathways. Chem5, 460–473 (2019). CASGoogle Scholar
  62. Badowski, T., Molga, K. & Grzybowski, B. A. Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans. Chem. Sci.10, 4640–4651 (2019). CASPubMedPubMed CentralGoogle Scholar
  63. Burke, K. Perspective on density functional theory. J. Chem. Phys.136, 150901 (2012). PubMedGoogle Scholar
  64. Chermette, H. Chemical reactivity indexes in density functional theory. J. Comput. Chem.20, 129–154 (1999). CASGoogle Scholar
  65. Hegde, G. & Bowen, R. C. Machine-learned approximations to density functional theory Hamiltonians. Sci. Rep.7, 42669 (2017). CASPubMedPubMed CentralGoogle Scholar
  66. Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci.8, 3192–3203 (2017). CASPubMedPubMed CentralGoogle Scholar
  67. Grisafi, A. et al. Transferable machine-learning model of the electron density. ACS Cent. Sci.5, 57–64 (2019). CASPubMedGoogle Scholar
  68. Sadowski, P., Fooshee, D., Subrahmanya, N. & Baldi, P. Synergies between quantum mechanics and machine learning in reaction prediction. J. Chem. Inf. Model.56, 2125–2128 (2016). CASPubMedGoogle Scholar
  69. Moosavi, S. M. et al. Capturing chemical intuition in synthesis of metal-organic frameworks. Nat. Commun.10, 539 (2019). CASPubMedPubMed CentralGoogle Scholar
  70. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature533, 73–76 (2016). CASPubMedGoogle Scholar
  71. Kayala, M. A., Azencott, C. A., Chen, J. H. & Baldi, P. Learning to predict chemical reactions. J. Chem. Inf. Model.51, 2209–2222 (2011). CASPubMedPubMed CentralGoogle Scholar
  72. Fooshee, D. et al. Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng.3, 442–452 (2018). CASGoogle Scholar
  73. Schwaller, P., Gaudin, T., Lanyi, 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). CASPubMedPubMed CentralGoogle Scholar
  74. Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci.2, 725–732 (2016). CASPubMedPubMed CentralGoogle Scholar
  75. Hughes, T. B., Dang, N. L., Miller, G. P. & Swamidass, S. J. Modeling reactivity to biological macromolecules with a deep multitask network. ACS Cent. Sci.2, 529–537 (2016). CASPubMedPubMed CentralGoogle Scholar
  76. Hughes, T. B., Miller, G. P. & Swamidass, S. J. Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS Cent. Sci.1, 168–180 (2015). CASPubMedPubMed CentralGoogle Scholar
  77. 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). CASPubMedPubMed CentralGoogle Scholar
  78. Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci.10, 370–377 (2019). CASPubMedGoogle Scholar
  79. Breiman, L. Random forests. Mach. Learn.45, 5–32 (2001). Google Scholar
  80. Ho, T. K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell.20, 832–844 (1998). Google Scholar
  81. Rodrigues, T. et al. De novo fragment design for drug discovery and chemical biology. Angew. Chem. Int. Ed.54, 15079–15083 (2015). CASGoogle Scholar
  82. Rodrigues, T. et al. Machine intelligence decrypts beta-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem. Sci.9, 6899–6903 (2018). CASPubMedPubMed CentralGoogle Scholar
  83. Richter, M. F. et al. Predictive compound accumulation rules yield a broad-spectrum antibiotic. Nature545, 299–304 (2017). CASPubMedPubMed CentralGoogle Scholar
  84. Wolfe, J. M. et al. Machine learning to predict cell-penetrating peptides for antisense delivery. ACS Cent. Sci.4, 512–520 (2018). CASPubMedPubMed CentralGoogle Scholar
  85. Chuang, K. V. & Keiser, M. J. Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”. Science362, eaat8603 (2018). PubMedGoogle Scholar
  86. Estrada, J. G., Ahneman, D. T., Sheridan, R. P., Dreher, S. D. & Doyle, A. G. Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”. Science362, eaat8763 (2018). PubMedGoogle Scholar
  87. Skoraczynski, G. et al. Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? Sci. Rep.7, 3582 (2017). CASPubMedPubMed CentralGoogle Scholar
  88. Chuang, K. V. & Keiser, M. J. Adversarial controls for scientific machine learning. ACS Chem. Biol.13, 2819–2821 (2018). CASPubMedGoogle Scholar
  89. Beker, W., Gajewska, E. P., Badowski, T. & Grzybowski, B. A. Prediction of major regio-, site-, and diastereoisomers in diels-alder reactions by using machine-learning: the importance of physically meaningful descriptors. Angew. Chem. Int. Ed.58, 4515–4519 (2019). CASGoogle Scholar
  90. Nielsen, M. K., Ahneman, D. T., Riera, O. & Doyle, A. G. Deoxyfluorination with sulfonyl fluorides: navigating reaction space with machine learning. J. Am. Chem. Soc.140, 5004–5008 (2018). CASPubMedGoogle Scholar
  91. Halford, G. S., Baker, R., McCredden, J. E. & Bain, J. D. How many variables can humans process? Psychol. Sci.16, 70–76 (2005). PubMedGoogle Scholar
  92. Leardi, R. Experimental design in chemistry: A tutorial. Anal. Chim. Acta652, 161–172 (2009). CASPubMedGoogle Scholar
  93. Murray, P. M. et al. The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry. Org. Biomol. Chem.14, 2373–2384 (2016). CASPubMedGoogle Scholar
  94. Austin, N. D., Sahinidis, N. V., Konstantinov, I. A. & Trahan, D. W. COSMO-based computer-aided molecular/mixture design: A focus on reaction solvents. AIChE J.63, 104–122 (2018). Google Scholar
  95. Struebing, H. et al. Computer-aided molecular design of solvents for accelerated reaction kinetics. Nat. Chem.5, 952–957 (2013). CASPubMedGoogle Scholar
  96. Truhlar, D. G. Chemical reactivity: Inverse solvent design. Nat. Chem.5, 902–903 (2013). CASPubMedGoogle Scholar
  97. Gao, H. et al. Using machine learning to predict suitable conditions for organic reactions. ACS Cent. Sci.4, 1465–1476 (2018). CASPubMedPubMed CentralGoogle Scholar
  98. Zhou, Z., Li, X. & Zare, R. N. Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci.3, 1337–1344 (2017). CASPubMedPubMed CentralGoogle Scholar
  99. Bedard, A. C. et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science361, 1220–1225 (2018). CASPubMedGoogle Scholar
  100. Reker, D. & Schneider, G. Active-learning strategies in computer-assisted drug discovery. Drug. Discov. Today20, 458–465 (2015). PubMedGoogle Scholar
  101. Reker, D., Schneider, P. & Schneider, G. Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors. Chem. Sci.7, 3919–3927 (2016). CASPubMedPubMed CentralGoogle Scholar
  102. Reker, D. & Brown, J. B. Selection of informative examples in chemogenomic datasets. Methods Mol. Biol.1825, 369–410 (2018). CASPubMedGoogle Scholar
  103. Reker, D., Schneider, P., Schneider, G. & Brown, J. B. Active learning for computational chemogenomics. Future Med. Chem.9, 381–402 (2017). CASPubMedGoogle Scholar
  104. Sans, V., Porwol, L., Dragone, V. & Cronin, L. A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy. Chem. Sci.6, 1258–1264 (2015). CASPubMedGoogle Scholar
  105. Häse, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. Phoenics: A Bayesian optimizer for chemistry. ACS Cent. Sci.4, 1134–1145 (2018). PubMedPubMed CentralGoogle Scholar
  106. Frazier, P. I. A tutorial on Bayesian optimization. Preprint at arXivhttps://arxiv.org/abs/1807.02811 (2018).
  107. Brochu, E., Cora, V. M. & Freitas, N. d. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Preprint at arXivhttps://arxiv.org/abs/1012.2599 (2010).
  108. Reker, D., Bernardes, G. J. L. & Rodrigues, T. Evolving and nano data enabled machine intelligence for chemical reaction optimization. Preprint at ChemRxivhttps://chemrxiv.org/articles/Evolving_and_Nano_Data_Enabled_Machine_Intelligence_for_Chemical_Reaction_Optimization/7291205/1 (2018).
  109. Granda, J. M., Donina, L., Dragone, V., Long, D. L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature559, 377–381 (2018). CASPubMedPubMed CentralGoogle Scholar
  110. Ahmadi, M., Vogt, M., Iyer, P., Bajorath, J. & Frohlich, H. Predicting potent compounds via model-based global optimization. J. Chem. Inf. Model.53, 553–559 (2013). CASPubMedGoogle Scholar
  111. Patil, P. C. & Luzzio, F. A. Synthesis of extended oxazoles II: Reaction manifold of 2-(halomethyl)-4,5-diaryloxazoles. Tetrahedron Lett.57, 757–759 (2016). CASPubMedPubMed CentralGoogle Scholar
  112. Blakemore, D. C. et al. Organic synthesis provides opportunities to transform drug discovery. Nat. Chem.10, 383–394 (2018). CASPubMedGoogle Scholar
  113. Roberts, R. M. Serendipity: Accidental Discoveries in Science 1-288 (John Wiley & Sons, 1989).
  114. Davey, S. Rapid reaction discovery. Nat. Chem.4, 69 (2012). CASGoogle Scholar
  115. McNally, A., Prier, C. K. & MacMillan, D. W. Discovery of an alpha-amino C–H arylation reaction using the strategy of accelerated serendipity. Science334, 1114–1117 (2011). CASPubMedPubMed CentralGoogle Scholar
  116. Amara, Z. et al. Automated serendipity with self-optimizing continuous-flow reactors. Eur. J. Org. Chem.2015, 6141–6145 (2015). CASGoogle Scholar
  117. Dragone, V., Sans, V., Henson, A. B., Granda, J. M. & Cronin, L. An autonomous organic reaction search engine for chemical reactivity. Nat. Commun.8, 15733 (2017). PubMedPubMed CentralGoogle Scholar
  118. Gromski, P. S., Henson, A. B., Granda, J. M. & Cronin, L. How to explore chemical space using algorithms and automation. Nat. Rev. Chem.3, 119–128 (2019). Google Scholar
  119. Cao, Y., Romero, J. & Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev.62, 6:1–6:20 (2019). Google Scholar
  120. Rodrigues, T. et al. Multidimensional de novo design reveals 5-HT2B2B receptor-selective ligands. Angew. Chem. Int. Ed.54, 1551–1555 (2015). CASGoogle Scholar
  121. Reutlinger, M., Rodrigues, T., Schneider, P. & Schneider, G. Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands. Angew. Chem. Int. Ed.53, 582–585 (2014). CASGoogle Scholar
  122. Ban, T. A. The role of serendipity in drug discovery. Dialogues Clin. Neurosci.8, 335–344 (2006). PubMedPubMed CentralGoogle Scholar
  123. Rosales, A. R. et al. Rapid virtual screening of enantioselective catalysts using CatVS. Nat. Catal.2, 41–45 (2019). CASGoogle Scholar
  124. Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science363, eaav2211 (2019). CASPubMedGoogle Scholar
  125. Caramelli, D. et al. Networking chemical robots for reaction multitasking. Nat. Commun.9, 3406 (2018). PubMedPubMed CentralGoogle Scholar
  126. Fitzpatrick, D. E., Maujean, T., Evans, A. C. & Ley, S. V. Across-the-world automated optimization and continuous-flow synthesis of pharmaceutical agents operating through a cloud-based server. Angew. Chem. Int. Ed.57, 15128–15132 (2018). CASGoogle Scholar
  127. Lavecchia, A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today20, 318–331 (2015). PubMedGoogle Scholar
  128. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature559, 547–555 (2018). CASPubMedGoogle Scholar
  129. Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science349, 255–260 (2015). CASPubMedGoogle Scholar
  130. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science361, 360–365 (2018). CASPubMedGoogle Scholar
  131. Wallach, I. & Heifets, A. Most ligand-based classification benchmarks reward memorization rather than generalization. J. Chem. Inf. Model.58, 916–932 (2018). CASPubMedGoogle Scholar

Acknowledgements

A.F.A. acknowledges Fundação para a Ciência e Tecnologia (FCT) Portugal for financial support through a PhD grant (PD/BD/143125/2019). T.R. is an investigador auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges FCT/FEDER (02/SAICT/2017, grant 28333) for funding. The authors thank the reviewers for their comments.