Expanding the medicinal chemistry synthetic toolbox

A Corrigendum to this article was published on 28 November 2018

Abstract

The key objectives of medicinal chemistry are to efficiently design and synthesize bioactive compounds that have the potential to become safe and efficacious drugs. Most medicinal chemistry programmes rely on screening compound collections populated by a range of molecules derived from a set of known and robust chemistry reactions. Analysis of the role of synthetic organic chemistry in subsequent hit and lead optimization efforts suggests that only a few reactions dominate. Thus, the uptake of new synthetic methodologies in drug discovery is limited. Starting from the known limitations of reaction parameters, synthesis design tools, synthetic strategies and innovative chemistries, here we highlight opportunities for the expansion of the medicinal chemists' synthetic toolbox. More intense crosstalk between synthetic and medicinal chemists in industry and academia should enable enhanced impact of new methodologies in future drug discovery.

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Figure 1: Common chemical reactions in drug discovery and development.
Figure 2: Examples of bioprocessing and natural product chemistry applied to bioactive or drug molecules.
Figure 3: Examples of emerging synthetic methodologies applied to fragments and lead-like scaffolds.
Figure 4: Broad synthetic strategies and platforms applied to lead and drug-like scaffolds.

References

  1. 1

    Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 3–50 (1996).

    CAS  PubMed  Article  Google Scholar 

  2. 2

    Mullard, A. The drug maker's guide to the galaxy. Nature 549, 445–447 (2017).

    PubMed  Article  Google Scholar 

  3. 3

    Ertl, P. J. Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties & automatic identification of drug-like bioisosteric groups. J. Chem. Inf. Comput. Sci. 43, 374–380 (2003).

    CAS  PubMed  Article  Google Scholar 

  4. 4

    Virshup, A. M., Contreras-García, J., Wipf, P., Yang, W. & Beratan, D. N. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J. Am. Chem. Soc. 135, 7296–7303 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5

    Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).

    CAS  PubMed  Article  Google Scholar 

  6. 6

    Bemis, G. W. & Murcko, M. A. Properties of known drugs. 2. Side chains. J. Med. Chem. 42, 5095–5099 (1999).

    CAS  PubMed  Article  Google Scholar 

  7. 7

    Wang, J. & Hou, T. Drug and drug candidate building block analysis. J. Chem. Inf. Model. 50, 55–67 (2010).

    PubMed  Article  CAS  Google Scholar 

  8. 8

    Taylor, R. D., MacCoss, M. & Lawson, A. D. Rings in drugs. J. Med. Chem. 57, 5845–5859 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9

    Taylor, R. D., MacCoss, M. & Lawson, A. D. Combining molecular scaffolds from FDA approved drugs: application to drug discovery. J. Med. Chem. 60, 1638–1647 (2017).

    CAS  PubMed  Article  Google Scholar 

  10. 10

    Pitt, W. R., Parry, D. M., Perry, B. G. & Groom, C. R. Heteroaromatic rings of the future. J. Med. Chem. 52, 2952–2963 (2009).

    CAS  PubMed  Article  Google Scholar 

  11. 11

    Visini, R., Arús-Pous, J., Awale, M. & Reymond, J. L. Virtual exploration of the ring systems chemical universe. J. Chem. Inf. Model. 57, 2707–2718 (2017).

    CAS  PubMed  Article  Google Scholar 

  12. 12

    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).

    CAS  PubMed  Article  Google Scholar 

  13. 13

    Brown, D. G. & Boström, J. Analysis of past and present synthetic methodologies on medicinal chemistry: where have all the new reactions gone? J. Med. Chem. 59, 4443–4458 (2016).

    CAS  PubMed  Article  Google Scholar 

  14. 14

    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).

    CAS  PubMed  Article  Google Scholar 

  15. 15

    Carey, J. S., Laffan, D., Thomson, C. & Williams, M. T. Analysis of the reactions used for the preparation of drug candidate molecules. Org. Biomol. Chem. 4, 2337–2347 (2006).

    CAS  PubMed  Article  Google Scholar 

  16. 16

    Dugger, R. W., Ragan, J. A. & Brown Ripin, D. H. Survey of GMP bulk reactions run in a research facility between 1985 and 2002. Org. Proc. Res. Dev. 9, 253–258 (2005).

    CAS  Article  Google Scholar 

  17. 17

    Satyanarayanajois, S. D. & Hill, R. A. Medicinal chemistry for 2020. Future Med. Chem. 14, 1765–1786 (2011).

    Article  CAS  Google Scholar 

  18. 18

    Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W. & Jhoti, H. Twenty years on: the impact of fragments on drug discovery. Nat. Rev. Drug Discov. 15, 605–619 (2016).

    CAS  PubMed  Article  Google Scholar 

  19. 19

    Boström, J., Grant, J. A., Fjellström, O., Thelin, A. & Gustafsson, D. Potent fibrinolysis inhibitor discovered by shape and electrostatic complementarity to the drug tranexamic acid. J. Med. Chem. 56, 3273–3280 (2013).

    PubMed  Article  CAS  Google Scholar 

  20. 20

    Lionta, E., Spyrou, G., Vassilatis, D. K. & Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top. Med. Chem. 14, 1923–1938 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    CAS  PubMed  Article  Google Scholar 

  22. 22

    Nadin, A., Hattotuwagama, C. & Churcher, I. Lead-oriented synthesis: a new opportunity for synthetic chemistry. Angew. Chem. Int. Ed. Engl. 51, 1114–1122 (2012).

    CAS  PubMed  Article  Google Scholar 

  23. 23

    Goldberg, F. W., Kettle, J. G., Kogej, T., Perry, M. W. & Tomkinson, N. P. Designing novel building blocks is an overlooked strategy to improve compound quality. Drug Discov. Today 20, 11–17 (2015).

    PubMed  Article  Google Scholar 

  24. 24

    Keserü, G. M. & Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nat. Rev. Drug Discov. 8, 203–212 (2009).

    PubMed  Article  CAS  Google Scholar 

  25. 25

    Young, R. J. & Leeson, P. D. Mapping the efficiency and physicochemical trajectories of successful optimizations. J. Med. Chem. https://doi.org/10.1021/acs.jmedchem.8b00180 (2018).

    CAS  PubMed  Article  Google Scholar 

  26. 26

    Keserü, G. M., Soós, T. & Kappe, C. O. Anthropogenic reaction parameters — the missing link between chemical intuition and the available chemical space. Chem. Soc. Rev. 43, 5387–5399 (2014).

    PubMed  Article  Google Scholar 

  27. 27

    Boström, J. & Brown, D. G. Stuck in a rut with old chemistry. Drug Discov. Today 21, 701–703 (2016).

    PubMed  Article  Google Scholar 

  28. 28

    Walters, W. P., Green, J., Weiss, J. R. & Murcko, M. A. What do medicinal chemists actually make? A 50-year retrospective. J. Med. Chem. 54, 6405–6416 (2011).

    CAS  PubMed  Article  Google Scholar 

  29. 29

    Blakemore, D. C. et al. Organic synthesis provides opportunities to transform drug discovery. Nat. Chem. 10, 383–394 (2018).

    CAS  PubMed  Article  Google Scholar 

  30. 30

    Hann, M. M. & Keserü, G. M. Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nat. Rev. Drug Discov. 11, 355–365 (2012).

    CAS  PubMed  Article  Google Scholar 

  31. 31

    Rafferty, M. F. No denying it: medicinal chemistry training is in big trouble. J. Med. Chem. 59, 10859–10864 (2016).

    CAS  PubMed  Article  Google Scholar 

  32. 32

    Campbell, I. B., Macdonald, S. J. F. & Procopiou, P. A. Medicinal chemistry in drug discovery in big pharma: past, present and future. Drug Discov. Today 23, 219–234 (2018).

    PubMed  Article  Google Scholar 

  33. 33

    Hartenfeller, M. et al. A collection of robust organic synthesis reactions for in silico molecule design. J. Chem. Inf. Model. 51, 3093–3098 (2011).

    CAS  PubMed  Article  Google Scholar 

  34. 34

    Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    CAS  PubMed  Article  Google Scholar 

  35. 35

    Bergman, R. G. & Danheiser, R. L. Reproducibility in chemical research. Angew. Chem. Int. Ed. Engl. 55, 12548–12549 (2016).

    CAS  PubMed  Article  Google Scholar 

  36. 36

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

    CAS  PubMed  Article  Google Scholar 

  37. 37

    Rahman, S. A. et al. Reaction decoder tool (RDT): extracting features from chemical reactions. Bioinformatics 32, 2065–2066 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38

    Buitrago Santanilla, A. et al. Organic chemistry. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).

    CAS  PubMed  Article  Google Scholar 

  39. 39

    Perera, D. et al. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 429–434 (2018).

    CAS  PubMed  Article  Google Scholar 

  40. 40

    Szymkuc´, S. et al. Computer-assisted synthetic planning: The end of the beginning. Angew. Chem. Int. Ed. Engl. 55, 5904–5937 (2016).

    PubMed  Article  CAS  Google Scholar 

  41. 41

    Segler, M. H. S. & Waller, M. P. Modelling chemical reasoning to predict and invent reactions. Chem. Eur. J. 23, 6118–6128 (2017).

    CAS  PubMed  Article  Google Scholar 

  42. 42

    Kayala, M. A. & Baldi, P. ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning. J. Chem. Inf. Model. 52, 2526–2540 (2012).

    CAS  PubMed  Article  Google Scholar 

  43. 43

    Kayala, M. A., Azencott, C.-A., Chen, J. H. & Baldi, P. Learning to predict chemical reactions. J. Chem. Inf. Model. 51, 2209–2222 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Stark, S. A., Neudert, R. & Threlfall, R. Wiley ChemPlanner predicts experimentally verified synthesis routes in medicinal chemistry. CHEManager http://www.chemanager-online.com/en/whitepaper/wiley-chemplanner-predicts-experimentally-verifiedsynthesis-routes-medicinal-chemistry (2016).

  45. 45

    Bøgevig, A. et al. Route design in the 21st century: the ICSYNTH Software tool as an idea generator for synthesis prediction. Org. Proc. Res. Dev. 19, 357–368 (2015).

    Article  CAS  Google Scholar 

  46. 46

    Klucznik, T. et al. Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem 4, 522–532 (2018).

    CAS  Article  Google Scholar 

  47. 47

    Kroman, J. C. et al. Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactions. Chem. Sci. 9, 660–665 (2018).

    Article  Google Scholar 

  48. 48

    Hansen, E. et al. Prediction of stereochemistry using Q2MM. Acc. Chem. Res. 49, 996–1005 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49

    Goldberg, F. W., Kettle, J. G., Kogej, T., Perry, M. W. & Tomkinson, N. P. Designing novel building blocks is an overlooked strategy to improve compound quality. Drug Discov. Today 20, 11–17 (2015).

    PubMed  Article  Google Scholar 

  50. 50

    Carreira, E. M. & Fessard, T. C. Four-membered ring-containing spirocycles: synthetic strategies and opportunities. Chem. Rev. 114, 8257–8322 (2014).

    CAS  PubMed  Article  Google Scholar 

  51. 51

    Helal, C. J. et al. Increased building block access through collaboration. Drug Discov. Today https://doi.org/10.1016/j.drudis.2018.03.001 (2018).

    PubMed  Article  Google Scholar 

  52. 52

    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).

    CAS  PubMed  Article  Google Scholar 

  53. 53

    Carlson, R. & Carlson, J. E. in Design and Optimization in Organic Synthesis Vol. 24 1–574 (Elsevier, 2005).

    Google Scholar 

  54. 54

    Cook, A. Computer-aided synthesis design: 40 years on — WIREs. Comput. Mol. Sci. 2, 79–107 (2012).

    CAS  Article  Google Scholar 

  55. 55

    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. arXiv https://arxiv.org/abs/1711.04810 (2017).

  56. 56

    Baskin, I., Madzhidov, T. I., Antipin, I. S. & Varnek, A. A. Artificial intelligence in synthetic chemistry: achievements and prospects. Rus. Chem. Rev. 86, 1127 (2017).

    CAS  Article  Google Scholar 

  57. 57

    Wengong, J. Predicting organic reaction outcomes with Weisfeiler-Lehman network. arXiv https://arxiv.org/abs/1709.04555 (2017).

  58. 58

    Fooshee, D. Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng. https://doi.org/10.1039/C7ME00107J (2018).

    CAS  Article  Google Scholar 

  59. 59

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60

    Brynjolfsson, E. & Mitchell, T. What can machine learning do? Workforce implications: profound change is coming, but roles for humans remain. Science 358, 1530–1534 (2017).

    CAS  PubMed  Article  Google Scholar 

  61. 61

    Griffen, E. J. et al. Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence? Drug Discov. Today 23, 1373–1384 (2018).

    PubMed  Article  Google Scholar 

  62. 62

    Fitzpatrick, D. E., Battilocchio, C. & Ley, S. V. Enabling technologies for the future of chemical synthesis. ACS Cent. Sci. 2, 131–138 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63

    Trang, J., T. T. T., Ermolat'ev, D. S. & Van der Eycken, E. V. Facile and diverse microwave-assisted synthesis of secondary propargylamines in water using CuCl/CuCl2. RSC Adv. 5, 28921–28924 (2015).

    CAS  Article  Google Scholar 

  64. 64

    Tsoung, J. et al. Synthesis of fused pyrimidinone and quinolone derivatives in an automated high-temperature and high-pressure flow reactor. J. Org. Chem. 82, 1073–1084 (2017).

    CAS  PubMed  Article  Google Scholar 

  65. 65

    Tran, D. N., Battilocchio, C., Lou, S.-B., Hawkins, J. M. & Ley, S. V. Flow chemistry as a discovery tool to access sp2–sp3 cross-coupling reactions via diazo compounds. Chem. Sci. 6, 1120–1125 (2015).

    CAS  PubMed  Article  Google Scholar 

  66. 66

    Battilocchio, C. et al. Iterative reactions of transient boronic acids enable sequential C–C bond formation. Nat. Chem. 8, 360–367 (2016).

    CAS  PubMed  Article  Google Scholar 

  67. 67

    Yoshida, J., Takahashi, Y. & Nagaki, A. Flash chemistry: flow chemistry that cannot be done in batch. Chem. Commun. 49, 9896–9904 (2013).

    CAS  Article  Google Scholar 

  68. 68

    Kim, H. et al. Submillisecond organic synthesis: outpacing Fries rearrangement through microfluidic rapid mixing. Science 352, 691–694 (2016).

    CAS  PubMed  Article  Google Scholar 

  69. 69

    Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).

    CAS  PubMed  Article  Google Scholar 

  70. 70

    Li, J. et al. Synthesis of many different types of organic small molecules using one automated process. Science 347, 1221–1226 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71

    Baranczak, A. et al. Integrated platform for expedited synthesis-pPurification-testing of small molecule libraries. ACS Med. Chem. Lett. 8, 461–465 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72

    Godfrey, A. G., Masquelin, T. & Hemmerle, H. A remote-controlled adaptive medchem lab: an innovative approach to enable drug discovery in the 21st Century. Drug Disc. Today 18, 795–802 (2013).

    CAS  Article  Google Scholar 

  73. 73

    Reizman, B. J., Wang, Y.-M., Buchwald, S. L. & Jensen, K. F. Suzuki-Miyaura cross-coupling optimization enabled by automated feedback. React. Chem. Eng. 1, 658–666 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74

    Trobe, M. & Burke, M. D. The molecular industrial revolution: automated synthesis of small molecules. Ang. Chem. Int. Ed. 57, 4192–4214 (2018).

    CAS  Article  Google Scholar 

  75. 75

    Troshin, K. & Hartwig, J. F. Snap deconvolution: an informatics approach to high-throughput discovery of catalytic reactions. Science 357, 175–181 (2017).

    CAS  PubMed  Article  Google Scholar 

  76. 76

    Chakravorty, S. J. et al. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Discov. 23, 532–545 (2018).

    CAS  PubMed  Google Scholar 

  77. 77

    Stalcup, A. M. Chiral separations. Annu. Rev. Anal. Chem. 3, 341–363 (2010).

    CAS  Article  Google Scholar 

  78. 78

    Desai, B. et al. Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. J. Med. Chem. 56, 3033–3047 (2013).

    CAS  PubMed  Article  Google Scholar 

  79. 79

    Czechtizky, W. et al. Integrated synthesis and testing of substituted xanthine based DPP4 inhibitors: application to drug discovery. ACS Med. Chem. Lett. 4, 768–772 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80

    Hobbs, A. N. & Young, R. J. Practical purification of hydrophilic fragments and lead/drug-like molecules by reverse phase flash chromatography: tips, tricks and contemporary developments. Drug Discov. Today 18, 148–154 (2013).

    CAS  PubMed  Article  Google Scholar 

  81. 81

    Buszewski, B. & Noga, S. Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique. Anal. Bioanal. Chem. 402, 231–247 (2012).

    CAS  PubMed  Article  Google Scholar 

  82. 82

    Hettiarachchi, K., Kong, M., Yun, A., Jacobsen, J. R. & Xue, Q. Development of an automated dual-mode supercritical fluid chromatography & reversed-phase liquid chromatography mass-directed purification system for small-molecule drug discovery. J. Sep. Sci. 37, 775–781 (2014).

    CAS  PubMed  Article  Google Scholar 

  83. 83

    Tarcsay, A., Nyíri, K., Keserü, G. M. Impact of lipophilic efficiency on compound quality. J. Med. Chem. 55, 1252–1260 (2012).

    CAS  PubMed  Article  Google Scholar 

  84. 84

    Johnson, T. W., Gallego, R. A., Edwards, M. P. Lipophilic efficiency as an important metric in drug design. J. Med. Chem. https://doi.org/10.1021/acs.jmedchem.8b00077 (2018).

    CAS  PubMed  Article  Google Scholar 

  85. 85

    Kan, S. B. J., Huang, X., Gumulya, Y., Chen, K. & Arnold, F. H. Genetically programmed chiral organoborane synthesis. Nature 552, 132–136 (2017).

    CAS  PubMed  Article  Google Scholar 

  86. 86

    Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. Engl. 57, 4143–4148 (2018).

    CAS  PubMed  Article  Google Scholar 

  87. 87

    Prier, C. K., Zhang, R. K., Buller, A. R., Brinkmann-Chen, S. & Arnold, F. H. Enantioselective, intermolecular benzylic C-H amination catalysed by an engineered iron-haem enzyme. Nat. Chem. 9, 629–634 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88

    Boer, J. et al. Roles of UGT, P450, and gut microbiota in the metabolism of epacadostat in humans. Drug Metab. Dispos. 44, 1668–1674 (2016).

    CAS  PubMed  Article  Google Scholar 

  89. 89

    Obach, R. S. et al. Lead diversification at the nanomole scale using liver microsomes and quantitative nuclear magnetic resonance spectroscopy: application to phosphodiesterase 2 inhibitors. J. Med. Chem. 61, 3626–3640 (2018).

    CAS  PubMed  Article  Google Scholar 

  90. 90

    Schonherr, H. & Cernak, T. Profound methyl effects in drug discovery and a call for new C-H methylation reactions. Angew. Chem. Int. Ed. Engl. 52, 12256–12267 (2013).

    PubMed  Article  CAS  Google Scholar 

  91. 91

    Gillis, E. P., Eastman, K. J., Hill, M. D., Donnelly, D. J. & Meanwell, N. A. Applications of fluorine in medicinal chemistry. J. Med. Chem. 58, 8315–8359 (2015).

    CAS  PubMed  Article  Google Scholar 

  92. 92

    Pettersson, M. et al. Quantitative assessment of the impact of fluorine substitution on P-glycoprotein (P-gp) mediated efflux, permeability, lipophilicity, and metabolic stability. J. Med. Chem. 59, 5284–5296 (2016).

    CAS  PubMed  Article  Google Scholar 

  93. 93

    Obach, R. S., Walker, G. S. & Brodney, M. A. Biosynthesis of fluorinated analogs of drugs using human cytochrome P450 enzymes followed by deoxyfluorination and quantitative nuclear magnetic resonance spectroscopy to improve metabolic stability. Drug Metab. Dispos. 44, 634–646 (2016).

    CAS  PubMed  Article  Google Scholar 

  94. 94

    Romero, N., A. & Nicewicz, D. A. Organic photoredox catalysis. Chem. Rev. 116, 10075–10166 (2016).

    CAS  PubMed  Article  Google Scholar 

  95. 95

    Ji, Y. et al. Innate C-H trifluormethylation of heterocycles. Proc. Natl Aacad. Sci. USA. 108, 14411–14415 (2011).

    CAS  Article  Google Scholar 

  96. 96

    Zuo, Z. et al. Merging photoredox with nickel catalysis: coupling of α-carbonyl sp3-carbons with aryl halides. Science 345, 437–440 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97

    Zhang, X. & MacMillan, D. W. Alcohols as latent coupling fragments for metallophotoredox catalysis: sp3-sp2 cross coupling of oxalates with aryl halides. J. Am. Chem. Soc 138, 13862–13865 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98

    Wang, Z., Herraiz, A. G., del Hoyo, A. M. & Suero, M. G. Generating carbyne equivalents with photoredox catalysis. Nature 554, 86–91 (2018).

    CAS  PubMed  Article  Google Scholar 

  99. 99

    Denisenko, A. V. et al. Photochemical synthesis of 3-azabicyclo[3.2.0]heptanes: advanced building blocks for drug discovery. J. Org. Chem. 82, 9627–9636 (2017).

    CAS  PubMed  Article  Google Scholar 

  100. 100

    Dirocco, D. A. et al. Late-stage functionalization of biologically active heterocycles through photoredox catalysis. Angew. Chem. Int. Ed. Engl. 53, 4802–4806 (2014).

    CAS  PubMed  Article  Google Scholar 

  101. 101

    Yan, M., Kawamata, Y. & Baran, P. S. Synthetic organic electrochemical methods since 2000: on the verge of a renaissance. Chem. Rev. 117, 13230–13319 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102

    Jiang, Y., Xu, K. & Zeng, C. Use of electrochemistry in the synthesis of heterocyclic structures. Chem. Rev. 118, 4485–4540 (2017).

    PubMed  Article  CAS  Google Scholar 

  103. 103

    Yoshida, J., Kataoka, K., Horcajada, R. & Nagaki, A. Modern strategies in electroorganic synthesis. Chem. Rev. 108, 2265–2299 (2008).

    CAS  PubMed  Article  Google Scholar 

  104. 104

    Morofuji, T., Shimizu, A. & Yoshida, J. Direct C-N coupling of imidazoles with aromatic and benzylic compounds via electrooxidative C-H functionalization. J. Am. Chem. Soc. 136, 4496–4499 (2014).

    CAS  PubMed  Article  Google Scholar 

  105. 105

    Zhao, H.-B., Hou, Z.-W., Liu, Z.-J., Zhou, Z.-F., Song, J. & Xu, H.-C. Amidinyl radical formation through anodic N-H bond cleavage and its application in aromatic C-H bond functionalization. Angew. Chem. Int. Ed. Engl. 56, 587–590 (2017).

    CAS  PubMed  Article  Google Scholar 

  106. 106

    Faust, M. R., Höfner, G., Pabel, J. & Wanner, K. T. Azetidine derivatives as novel γ -aminobutyric acid uptake inhibitors: synthesis, biological evaluation, and structure-activity relationships. Eur. J. Med. Chem. 45, 2453–2466 (2010).

    CAS  PubMed  Article  Google Scholar 

  107. 107

    Elsler, B., Schollmeyer, D., Dyballa, K. M., Franke, R. & Waldvogel, S. R. Metal- and reagent-free highly selective anodic cross-coupling reactions of phenols. Angew. Chem. Int. Ed. Engl. 53, 5210–5213 (2014).

    CAS  PubMed  Article  Google Scholar 

  108. 108

    Green, R. A., Brown, R. C. D. & Pletcher, D. A microflow electrolysis cell for laboratory synthesis on a multigram scale. Org. Process Res. Dev. 19, 1424–1427 (2015).

    CAS  Article  Google Scholar 

  109. 109

    Ajami, A. Converging trends brings organic electrochemistry to the front line of drug discovery. BiopharmaTrend.com www.biopharmatrend.com/post/40-converging-trends-brings-organic-electrochemistry-to-the-front-line-of-drug-discovery/ (2017).

  110. 110

    Cernak, T., Dykstra, K. D., Tyagarajan, S., Vachal, P. & Krska, S. W. The medicinal chemist's toolbox for late stage functionalization of drug-like molecules. Chem. Soc. Rev. 45, 546–576 (2016).

    CAS  PubMed  Article  Google Scholar 

  111. 111

    Wang, P. et al. Ligand-accelerated non-directed C-H functionalization of arenes. Nature 551, 489–493 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. 112

    Loh, Y. Y. et al. Photoredox-catalyzed deuteration and tritiation of pharmaceutical compounds. Science 358, 1182–1187 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113

    Dai, H.-X., Stepan, A. F., Plummer, M. S., Zhang, Y.-H. & Yu, J.-Q. Divergent C-H functionalizations directed by sulfonamide pharmacophores: late-stage diversification as a tool for drug discovery. J. Am. Chem. Soc. 133, 7222–7228 (2011).

    CAS  PubMed  Article  Google Scholar 

  114. 114

    Wei, X. et al. Arylation and enantioselective hydrogenation enables ideal asymmetric entry to the indenopiperidine core of an 11β-HSD-1 inhibitor. J. Am. Chem. Soc. 138, 15473–15481 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115

    Li, C. et al. Decarboxylative borylation. Science 356, eaam7355 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. 116

    Lo, J. C., Yabe, Y. & Baran, P. S. A practical and catalytic reductive olefin coupling reaction. J. Am. Chem. Soc. 136, 1304–1307 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117

    Lu, X. et al. Practical carbon-carbon bond formation from olefins through nickel-catalyzed reductive olefin hydrocarbonation. Nat. Commun. 7, 11129 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  118. 118

    Lopchuk, J. M. et al. Strain-release heteroatom functionalization: development, scope, and stereospecificity. J. Am. Chem. Soc. 139, 3209–3226 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119

    Ariki, Z. T., Maekawa, Y., Nambo, M. & Crudden, C. M. Preparation of quaternary centers via nickel-catalyzed Suzuki–Miyaura cross-coupling of tertiary sulfones. J. Am. Chem. Soc. 140, 78–81 (2018).

    CAS  PubMed  Article  Google Scholar 

  120. 120

    Campbell, P. S., Jamieson, C., Simpson, I. & Watson, A. J. B. Practical synthesis of pharmaceutically relevant molecules enriched in sp3 character. Chem. Commun. 54, 46–49 (2018).

    CAS  Article  Google Scholar 

  121. 121

    Ritchie, T. J. & Macdonald, S. J. Physicochemical descriptors of aromatic character and their use in drug discovery. J. Med. Chem. 57, 7206–7215 (2014).

    CAS  PubMed  Article  Google Scholar 

  122. 122

    Kolb, H. C., Finn, M. G. & Sharpless, K. B. Click chemistry: diverse chemical function from a few good reactions. Angew. Chem. Int. Ed. Engl. 40, 2004–2021 (2001).

    CAS  PubMed  Article  Google Scholar 

  123. 123

    Li, L. et al. Design of an amide N-glycoside derivative of β-glucogallin: a stable, potent, and specific inhibitor of aldose reductase. J. Med. Chem. 57, 71–77 (2014).

    CAS  PubMed  Article  Google Scholar 

  124. 124

    Tyler, D. S. et al. Click chemistry enables preclinical evaluation of targeted epigenetic therapies. Science 356, 1397–1401 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. 125

    Dömling, A., Wang, W. & Wang, K. Chemistry and biology of multicomponent reactions. Chem. Rev. 112, 3083–3135 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. 126

    Osipova, A., Yufit, D. S. & De Meijere, A. Synthesis of new cyclopropylisonitriles and their applications in Ugi four-component reactions. Synthesis 1, 131–139 (2007).

    Google Scholar 

  127. 127

    Liddle, J. et al. The discovery of GSK221149A: a potent and selective oxytocin antagonist. Bioorg. Med. Chem. Lett. 18, 90–94 (2008).

    CAS  PubMed  Article  Google Scholar 

  128. 128

    Zarganes-Tzitzikas, T. & Dömling, A. Modern multicomponent reactions for better drug syntheses. Org. Chem. Front. 1, 834–837 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129

    Goodnow, R. A. & Davie, C. P. DNA-encoded library technology: a brief guide to its evolution and impact on drug discovery. Annu. Rep. Med. Chem. 50, 1–15 (2017).

    CAS  Google Scholar 

  130. 130

    Arico-Muendel, C. C. From haystack to needle: finding value with DNA encoded library technology at GSK. MedChemComm 7, 1898–1909 (2016).

    CAS  Article  Google Scholar 

  131. 131

    Satz, A. L. et al. DNA Compatible multistep synthesis and applications to DNA encoded libraries. Bioconj. Chem. 26, 1623–1632 (2015).

    CAS  Article  Google Scholar 

  132. 132

    Thomas, B. et al. Application of biocatalysis to on-DNA carbohydrate library synthesis. Chembiochem 18, 858–863 (2017).

    CAS  PubMed  Article  Google Scholar 

  133. 133

    Goodnow Jr, R. A., Dumelin, C. E. & Keefe, A. D. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nat. Rev. Drug Discov. 16, 131–147 (2017).

    Article  CAS  Google Scholar 

  134. 134

    Harris, P. A. et al. DNA-encoded library screening identifies benzo[b][1,4]oxazepin-4-ones as highly potent and monoselective receptor interacting protein 1 kinase inhibitors. J. Med. Chem. 59, 2163–2178 (2016).

    CAS  PubMed  Article  Google Scholar 

  135. 135

    Harris, P. A. et al. Discovery of a first-in-class receptor interacting protein 1 (RIP1) kinase specific clinical candidate (GSK2982772) for the treatment of inflammatory diseases. J. Med. Chem. 60, 1247–1261 (2017).

    CAS  PubMed  Article  Google Scholar 

  136. 136

    Soutter, H. H. et al. Discovery of cofactor-specific, bactericidal Mycobacterium tuberculosis InhA inhibitors using DNA-encoded library technology. Proc. Natl Acad. Sci. USA 113, E7880–E7889 (2016).

    CAS  PubMed  Article  Google Scholar 

  137. 137

    Chan, A. I. et al. Discovery of a covalent kinase inhibitor from a DNA-encoded small-molecule library × protein library selection. J. Am. Chem. Soc. 139, 10192–10195 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138

    Machutta, C. A. et al. Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening. Nat. Comm. 8, 16081 (2017).

    CAS  Article  Google Scholar 

  139. 139

    Bollag, G. et al. Vemurafenib: the first drug approved for BRAF-mutant cancer. Nat. Rev. Drug Discov. 11, 873–886 (2012).

    CAS  PubMed  Article  Google Scholar 

  140. 140

    Souers, A. J. et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208 (2013).

    CAS  PubMed  Article  Google Scholar 

  141. 141

    Perera, T. P. S. et al. Discovery and pharmacological characterization of JNJ-42756493 (Erdafitinib), a functionally celective small-molecule FGFR family inhibitor. Mol. Cancer Ther. 16, 1010–1020 (2017).

    CAS  PubMed  Article  Google Scholar 

  142. 142

    Keserü, G. M. et al. Design principles for fragment libraries: maximizing the value of learnings from pharma fragment-based drug discovery (FBDD) programs for use in academia. J. Med. Chem. 59, 8189–8206 (2016).

    PubMed  Article  CAS  Google Scholar 

  143. 143

    Palmer, N., Peakman, T. M., Norton, D. & Rees, D. C. Design and synthesis of dihydroisoquinolones for fragment-based drug discovery (FBDD). Org. Biomol. Chem. 14, 1599–1610 (2016).

    CAS  PubMed  Article  Google Scholar 

  144. 144

    Morley, A. D. et al. Fragment-based hit identification: thinking in 3D. Drug Discov. Today 18, 1221–1227 (2013).

    PubMed  Article  Google Scholar 

  145. 145

    Rizzo, S., Wakchaure, V. & Waldmann, H. in Natural Products in Medicinal Chemistry (ed. Hanessian, S.) Vol. 60 43–80 (Wiley-VCH Verlag GmbH & Co. KGaA, 2014).

    Google Scholar 

  146. 146

    Hall, R. J., Mortenson, P. N. & Murray, C. W. Efficient exploration of chemical space by fragment-based screening. Progr. Biophys. Mol. Biol. 116, 82–91 (2014).

    CAS  Article  Google Scholar 

  147. 147

    Ferenczy, G. G. & Keserü, G. M. How are fragments optimized? A retrospective analysis of 145 fragment optimizations. J. Med. Chem. 56, 2478–2486 (2013).

    CAS  PubMed  Article  Google Scholar 

  148. 148

    Kathman, S. G. & Statsyuk, A. V. Covalent tethering of fragments for covalent probe discovery. Med. Chem. Commun. 7, 576–585 (2016).

    CAS  Article  Google Scholar 

  149. 149

    Backus, K. M. et al. Proteome-wide covalent ligand discovery in native biological systems. Nature 534, 570–574 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150

    Parker, C. G. et al. Ligand and target discovery by fragment-based screening in human cells. Cell 168, 527–541 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. 151

    Wetzel, S., Lachance, H. & Waldmann, H. in Comprehensive in Natural Products II (eds Mander, L. & Liu, H.-W.) 5–46 (Elsevier, 2010).

    Google Scholar 

  152. 152

    Zaid, H., Raiyn, J., Nasser, A., Saad, B. & Rayan, A. Physicochemical properties of natural based products versus synthetic chemicals. Open Nutraceuticals J. 3, 194–202 (2010).

    CAS  Google Scholar 

  153. 153

    Li, J. W.-H. & Vederas, J. C. Drug discovery and natural products: end of an era or an endless frontier? Science 325, 161–165 (2009).

    PubMed  Article  CAS  Google Scholar 

  154. 154

    Gerry, C. J. & Schreiber, S. L. Chemical probes and drug leads from advances in synthetic planning and methodology. Nat. Rev. Drug Discov. 17, 333–352 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  155. 155

    Pascolutti, M. & Quinn, R. J. Natural products as lead structures: chemical transformations to create lead-like libraries. Drug Discov. Today 19, 215–221 (2014).

    CAS  PubMed  Article  Google Scholar 

  156. 156

    Karawajczyk, A. et al. Expansion of chemical space for collaborative lead generation and drug discovery: the European Lead Factory Perspective. Drug Discov. Today 20, 1310–1316 (2015).

    CAS  PubMed  Article  Google Scholar 

  157. 157

    Colomer, I. et al. A divergent synthetic approach to diverse molecular scaffolds: assessment of lead-likeness using LLAMA, an open-access computational tool. Chem. Commun. 52, 7209–7212 (2016).

    CAS  Article  Google Scholar 

  158. 158

    Foley, D. J., Nelson, A. & Marsden, S. P. Evaluating new chemistry to drive molecular discovery: fit for purpose? Angew. Chem. Int. Ed. Engl. 55, 13650–13657 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  159. 159

    Chow, S. Y. & Nelson, A. Embarking on a chemical space odyssey. J. Med. Chem. 60, 3591–3593 (2017).

    CAS  PubMed  Article  Google Scholar 

  160. 160

    Dow, M., Fisher, M., James, T., Marchetti, F. & Nelson, A. Towards the systematic exploration of chemical space. Org. Biomol. Chem. 10, 17–28 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  161. 161

    Morgentin, R. et al. Translation of innovative chemistry into screening libraries: an exemplar partnership from the European Lead Factory. Drug Discov. Today https://doi.org/10.1016/j.drudis.2018.05.007 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  162. 162

    Doveston, R., Marsden, S. & Nelson, A. Towards the realisation of lead-oriented synthesis. Drug Discov. Today 19, 813–819 (2014).

    CAS  PubMed  Article  Google Scholar 

  163. 163

    Mayol-Llinàs, J., Nelson, A., Farnaby, W. & Ayscough, A. Assessing molecular scaffolds for CNS drug discovery. Drug Discov. Today 22, 965–969 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  164. 164

    Dow, M. et al. Modular synthesis of diverse natural product-like macrocycles: discovery of hits with antimycobacterial activity. Chem. Eur. J. 23, 7207–7211 (2017).

    CAS  PubMed  Article  Google Scholar 

  165. 165

    Karageorgis, G., Warriner, S. & Nelson, A. Efficient discovery of bioactive scaffolds by activity-directed synthesis. Nat. Chem. 6, 872–876 (2014).

    CAS  PubMed  Article  Google Scholar 

  166. 166

    Karageorgis, G., Dow, M., Aimon, A., Warriner, S. & Nelson, A. Activity-directed synthesis with intermolecular reactions: Development of a fragment into a range of androgen receptor agonists. Angew. Chem. Int. Ed. Engl. 54, 13538–13544 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167

    Bootwicha, T., Feilner, J. M., Myers, E. L. & Aggarwal, V. K. Iterative assembly line synthesis of polypropionates with full stereocontrol. Nat. Chem. 9, 896 (2017).

    CAS  PubMed  Article  Google Scholar 

  168. 168

    Balieu, S. et al. Toward ideality: the synthesis of (+)-kalkitoxin and (+)-hydroxyphthioceranic acid by assembly-line synthesis. J. Am. Chem. Soc. 137, 4398–4403 (2015).

    CAS  PubMed  Article  Google Scholar 

  169. 169

    Ardkhean, R. et al. Cascade polycyclizations in natural product synthesis. Chem. Soc. Rev. 45, 1557–1569 (2016).

    CAS  PubMed  Article  Google Scholar 

  170. 170

    Dückert, H. et al. Natural product-inspired cascade synthesis yields modulators of centrosome integrity. Nat. Chem. Biol. 8, 179 (2011).

    PubMed  Article  CAS  Google Scholar 

  171. 171

    Schafroth, M. A., Zuccarello, G., Krautwald, S., Sarlah, D. & Carreira, E. M. Stereodivergent total synthesis of Δ9-tetrahydrocannabinols. Angew. Chem. Int. Ed. Engl. 53, 13898–13901 (2014).

    CAS  PubMed  Article  Google Scholar 

  172. 172

    Davis, A. M., Plowright, A. T. & Valeur, E. Directing evolution: the next revolution in drug discovery? Nat. Rev. Drug Disc. 16, 681 (2017).

    CAS  Article  Google Scholar 

  173. 173

    Shen, X., Corey, Chemistry, D. R. mechanism and clinical status of antisense oligonucleotides and duplex RNAs. Nucl. Ac. Res. 46, 1584–1600 (2018).

    CAS  Article  Google Scholar 

  174. 174

    Lai, A. C. & Crews, C. M. Induced protein degradation: an emerging drug discovery paradigm. Nat. Rev. Drug Discov. 16, 101–114 (2017).

    CAS  PubMed  Article  Google Scholar 

  175. 175

    Sternbach, L. The benzodiazepine story. J. Med. Chem. 22, 1–7 (1979).

    CAS  PubMed  Article  Google Scholar 

  176. 176

    Wright, P. M., Seiple, I. B. & Myers, A. G. The evolving role of chemical synthesis in antibacterial drug discovery. Angew. Chem. Int. Ed. Engl. 34, 8840–8869 (2014).

    Article  CAS  Google Scholar 

  177. 177

    Flam, F. The race to synthesize taxol ends in a tie. Science 263, 910–911 (1994).

    Article  Google Scholar 

  178. 178

    Donehower, R. C. The clinical development of paclitaxel: a successful collaboration of academia, industry and the national cancer institute. Oncol. 1, 240–243 (1996).

    CAS  Google Scholar 

  179. 179

    Ringel, M., Tollman, P., Hersch, G. & Schulze, U. Does size matter in R&D productivity? If not, what does? Nat. Rev. Drug Disc. 12, 901–902 (2013).

    CAS  Article  Google Scholar 

  180. 180

    Besnard, J., Jones, P. S., Hopkins, A. L. & Pannifer, A. D. The Joint European Compound Library: boosting precompetitive research. Drug Discov. Today 20, 181–186 (2015).

    CAS  PubMed  Article  Google Scholar 

  181. 181

    Collins, K. D. & Glorius, F. A robustness screen for the rapid assessment of chemical reactions. Nat. Chem. 5, 597–601 (2013).

    CAS  PubMed  Article  Google Scholar 

  182. 182

    Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today. 23, 1241–1250 (2018).

    PubMed  Article  Google Scholar 

  183. 183

    Fleming, N. How artificial intelligence is changing drug discovery. Nature 557, S55–S57 (2018).

    CAS  PubMed  Article  Google Scholar 

  184. 184

    Macdonald, S. J., Fray, M. J. & McInally, T. Passing on the medicinal chemistry baton: training undergraduates to be industry-ready through research projects between the University of Nottingham and GlaxoSmithKline. Drug Discov. Today 21, 880–887 (2016).

    PubMed  Article  Google Scholar 

  185. 185

    Urquhart, L. Market watch: top drugs and companies by sales in 2017. Nat. Rev. Drug Discov. 17, 232 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  186. 186

    Cernak, T. et al. Nanoscale synthesis and affinity ranking Nature 557, 228–232 (2018).

    PubMed  Article  CAS  Google Scholar 

  187. 187

    Pant, S. M. et al. Design, synthesis and testing of potent, selective hepsin inhibitors via application of an automated closed-loop optimization platform. J. Med. Chem. 61, 4335–4347 (2018).

    CAS  PubMed  Article  Google Scholar 

  188. 188

    Cole, K. P. et al. Kilogram-scale prexasertib monolactate monohydrate synthesis under continuous-flow CGMP conditions. Science 356, 1144–1150 (2017).

    CAS  PubMed  Article  Google Scholar 

  189. 189

    Cooper, T. W., Campbell, I. B. & Macdonald, S. J. Factors determining the selection of organic reactions by medicinal chemists and the use of these reactions in arrays (small focused libraries). Angew. Chem. Int. Ed. Engl. 49, 8082–8091 (2010).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

G.M.K. is supported by the National Brain Research Program (2017–1.2.1-NKP-2017-00002) of the National Research, Development and Innovation Office, Hungary.

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Glossary

Chemical space

Chemical space is a nebulous term used in various ways, but pertinent to drug discovery, it is the classification of molecules in terms of their physicochemical make-up, such as size, shape, lipophilicity, charge and hydrogen-bonding potential, which together can be used to describe the chemical space occupied.

Design–make–test–analyse (DMTA) cycle

The iterative central process in lead optimization, involving a cycle of four steps: design (a hypothesis is constructed to improve the profile of the lead molecule); make (compounds exemplifying the design are synthesized); test (synthesized compounds of confirmed structure and purity are tested in one or more carefully constructed and controlled assays); and analyse (the experimental data are analysed, and the results are used to amend a design hypothesis for the next cycle).

Drug-like compounds

Drug-likeness is another term that is used in various ways, often to describe the possession of physicochemical properties that are typical of orally absorbed small-molecule drugs. Lipinski's rule of five (Ro5) is one common metric; if no more than one of the following criteria is exceeded, then there should be a reasonable chance of oral bioavailability: molecular mass <500 Da, cLogP <5, number of hydrogen-bond donors <5 and number of hydrogen-bond acceptors <10. More recently, it has been demonstrated that oral activity is feasible beyond Ro5, and these programmes follow specific principles that contribute to the oral bioavailability.

Fragment

A simple, small and relatively polar molecule with 8–17 heavy atoms, often screened using sensitive biophysical techniques (such as X-ray crystallography, NMR spectroscopy and surface plasmon resonance) to identify inherently weak binders that can be elaborated into lead compounds.

Functional group tolerance

The range of organic functionalities that do not react with or impede the reagents and/or catalysts involved in a transformation. As drug molecules are predisposed to contain charged or hydrogen-bonding motifs to, for example, achieve potency and selectivity, this can often cause issues and interfere with catalysts, ligands and reactive partners.

Lead-like

Lead-likeness is a term that describes an aspirational profile for a screening collection of molecules that have physicochemical properties, together with predicted safety, pharmacokinetic and pharmacodynamic data and complexity, that bridge fragment space and drug-like space, as well as appropriate chemical functionalities that can be used in the optimization of the molecules into candidate drugs.

Quality of drug candidates

Like chemical space, the notion of compound quality is used in various ways, but physicochemical parameters can predict the likely quality of a compound, in conjunction with pharmacokinetic and pharmacodynamic data, giving confidence in probable exposure, efficacy and safety. This should not be prescriptive, but more optimal properties indicate a higher likelihood of success. Note that the actual set of physicochemical parameters is dependent on the target, the compartment where the target is engaged and the route of administration.

Robust reactions

Reproducible chemical transformations applicable to structurally diverse substrates, tolerating a range of functionality and able to be realized on simple equipment in a reasonable time period. Factors for robust reactions for medicinal chemistry include the following:

• Provide structures relevant for drug discovery

• Technically straightforward (no special equipment needed)

• Moderately sensitive to reaction parameters

• Broad applicability (also with polar substrates)

• Broad availability of starting materials and reagents

• Broad functional group tolerance, including polar functionalities

• Time for delivery of the target compounds is reasonably short (<1 month ideally)

• Simple operational procedure (minimal training and support needed)

• Low-risk reagents to comply with often onerous local safety rules

A full-size poster depicting the set of most popular robust reactions (available online for downloading; see Supplementary Fig. 1) illustrates their impact on drug discovery. Our hope is that displaying this poster in offices and laboratories could highlight the importance of expanding the medicinal chemistry synthetic toolbox and stimulate debate.

Structurally diverse substrates

The breadth of diversity of a given reaction type is dependent on the accessibility and intrinsic reactivity of the substrates and/or building blocks involved in the reactions. A reaction that can use a number of different reactive groups can be advantageous to medicinal chemists, as it will allow access to more analogues.

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Boström, J., Brown, D., Young, R. et al. Expanding the medicinal chemistry synthetic toolbox. Nat Rev Drug Discov 17, 709–727 (2018). https://doi.org/10.1038/nrd.2018.116

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