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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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).
Mullard, A. The drug maker's guide to the galaxy. Nature 549, 445–447 (2017).
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).
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).
Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).
Bemis, G. W. & Murcko, M. A. Properties of known drugs. 2. Side chains. J. Med. Chem. 42, 5095–5099 (1999).
Wang, J. & Hou, T. Drug and drug candidate building block analysis. J. Chem. Inf. Model. 50, 55–67 (2010).
Taylor, R. D., MacCoss, M. & Lawson, A. D. Rings in drugs. J. Med. Chem. 57, 5845–5859 (2014).
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).
Pitt, W. R., Parry, D. M., Perry, B. G. & Groom, C. R. Heteroaromatic rings of the future. J. Med. Chem. 52, 2952–2963 (2009).
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).
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).
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).
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).
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).
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).
Satyanarayanajois, S. D. & Hill, R. A. Medicinal chemistry for 2020. Future Med. Chem. 14, 1765–1786 (2011).
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).
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).
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).
Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).
Nadin, A., Hattotuwagama, C. & Churcher, I. Lead-oriented synthesis: a new opportunity for synthetic chemistry. Angew. Chem. Int. Ed. Engl. 51, 1114–1122 (2012).
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).
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).
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).
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).
Boström, J. & Brown, D. G. Stuck in a rut with old chemistry. Drug Discov. Today 21, 701–703 (2016).
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).
Blakemore, D. C. et al. Organic synthesis provides opportunities to transform drug discovery. Nat. Chem. 10, 383–394 (2018).
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).
Rafferty, M. F. No denying it: medicinal chemistry training is in big trouble. J. Med. Chem. 59, 10859–10864 (2016).
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).
Hartenfeller, M. et al. A collection of robust organic synthesis reactions for in silico molecule design. J. Chem. Inf. Model. 51, 3093–3098 (2011).
Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).
Bergman, R. G. & Danheiser, R. L. Reproducibility in chemical research. Angew. Chem. Int. Ed. Engl. 55, 12548–12549 (2016).
Engkvist, O. et al. Computational prediction of chemical reactions: current status and outlook. Drug Discov. Today 23, 1203–1218 (2018).
Rahman, S. A. et al. Reaction decoder tool (RDT): extracting features from chemical reactions. Bioinformatics 32, 2065–2066 (2016).
Buitrago Santanilla, A. et al. Organic chemistry. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).
Perera, D. et al. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 429–434 (2018).
Szymkuc´, S. et al. Computer-assisted synthetic planning: The end of the beginning. Angew. Chem. Int. Ed. Engl. 55, 5904–5937 (2016).
Segler, M. H. S. & Waller, M. P. Modelling chemical reasoning to predict and invent reactions. Chem. Eur. J. 23, 6118–6128 (2017).
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).
Kayala, M. A., Azencott, C.-A., Chen, J. H. & Baldi, P. Learning to predict chemical reactions. J. Chem. Inf. Model. 51, 2209–2222 (2011).
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).
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).
Klucznik, T. et al. Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem 4, 522–532 (2018).
Kroman, J. C. et al. Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactions. Chem. Sci. 9, 660–665 (2018).
Hansen, E. et al. Prediction of stereochemistry using Q2MM. Acc. Chem. Res. 49, 996–1005 (2016).
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).
Carreira, E. M. & Fessard, T. C. Four-membered ring-containing spirocycles: synthetic strategies and opportunities. Chem. Rev. 114, 8257–8322 (2014).
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).
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).
Carlson, R. & Carlson, J. E. in Design and Optimization in Organic Synthesis Vol. 24 1–574 (Elsevier, 2005).
Cook, A. Computer-aided synthesis design: 40 years on — WIREs. Comput. Mol. Sci. 2, 79–107 (2012).
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).
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).
Wengong, J. Predicting organic reaction outcomes with Weisfeiler-Lehman network. arXiv https://arxiv.org/abs/1709.04555 (2017).
Fooshee, D. Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng. https://doi.org/10.1039/C7ME00107J (2018).
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).
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).
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).
Fitzpatrick, D. E., Battilocchio, C. & Ley, S. V. Enabling technologies for the future of chemical synthesis. ACS Cent. Sci. 2, 131–138 (2016).
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).
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).
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).
Battilocchio, C. et al. Iterative reactions of transient boronic acids enable sequential C–C bond formation. Nat. Chem. 8, 360–367 (2016).
Yoshida, J., Takahashi, Y. & Nagaki, A. Flash chemistry: flow chemistry that cannot be done in batch. Chem. Commun. 49, 9896–9904 (2013).
Kim, H. et al. Submillisecond organic synthesis: outpacing Fries rearrangement through microfluidic rapid mixing. Science 352, 691–694 (2016).
Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).
Li, J. et al. Synthesis of many different types of organic small molecules using one automated process. Science 347, 1221–1226 (2015).
Baranczak, A. et al. Integrated platform for expedited synthesis-pPurification-testing of small molecule libraries. ACS Med. Chem. Lett. 8, 461–465 (2017).
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).
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).
Trobe, M. & Burke, M. D. The molecular industrial revolution: automated synthesis of small molecules. Ang. Chem. Int. Ed. 57, 4192–4214 (2018).
Troshin, K. & Hartwig, J. F. Snap deconvolution: an informatics approach to high-throughput discovery of catalytic reactions. Science 357, 175–181 (2017).
Chakravorty, S. J. et al. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Discov. 23, 532–545 (2018).
Stalcup, A. M. Chiral separations. Annu. Rev. Anal. Chem. 3, 341–363 (2010).
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).
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).
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).
Buszewski, B. & Noga, S. Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique. Anal. Bioanal. Chem. 402, 231–247 (2012).
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).
Tarcsay, A., Nyíri, K., Keserü, G. M. Impact of lipophilic efficiency on compound quality. J. Med. Chem. 55, 1252–1260 (2012).
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).
Kan, S. B. J., Huang, X., Gumulya, Y., Chen, K. & Arnold, F. H. Genetically programmed chiral organoborane synthesis. Nature 552, 132–136 (2017).
Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. Engl. 57, 4143–4148 (2018).
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).
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).
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).
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).
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).
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).
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).
Romero, N., A. & Nicewicz, D. A. Organic photoredox catalysis. Chem. Rev. 116, 10075–10166 (2016).
Ji, Y. et al. Innate C-H trifluormethylation of heterocycles. Proc. Natl Aacad. Sci. USA. 108, 14411–14415 (2011).
Zuo, Z. et al. Merging photoredox with nickel catalysis: coupling of α-carbonyl sp3-carbons with aryl halides. Science 345, 437–440 (2014).
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).
Wang, Z., Herraiz, A. G., del Hoyo, A. M. & Suero, M. G. Generating carbyne equivalents with photoredox catalysis. Nature 554, 86–91 (2018).
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).
Dirocco, D. A. et al. Late-stage functionalization of biologically active heterocycles through photoredox catalysis. Angew. Chem. Int. Ed. Engl. 53, 4802–4806 (2014).
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).
Jiang, Y., Xu, K. & Zeng, C. Use of electrochemistry in the synthesis of heterocyclic structures. Chem. Rev. 118, 4485–4540 (2017).
Yoshida, J., Kataoka, K., Horcajada, R. & Nagaki, A. Modern strategies in electroorganic synthesis. Chem. Rev. 108, 2265–2299 (2008).
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).
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).
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).
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).
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).
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).
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).
Wang, P. et al. Ligand-accelerated non-directed C-H functionalization of arenes. Nature 551, 489–493 (2017).
Loh, Y. Y. et al. Photoredox-catalyzed deuteration and tritiation of pharmaceutical compounds. Science 358, 1182–1187 (2017).
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).
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).
Li, C. et al. Decarboxylative borylation. Science 356, eaam7355 (2017).
Lo, J. C., Yabe, Y. & Baran, P. S. A practical and catalytic reductive olefin coupling reaction. J. Am. Chem. Soc. 136, 1304–1307 (2014).
Lu, X. et al. Practical carbon-carbon bond formation from olefins through nickel-catalyzed reductive olefin hydrocarbonation. Nat. Commun. 7, 11129 (2016).
Lopchuk, J. M. et al. Strain-release heteroatom functionalization: development, scope, and stereospecificity. J. Am. Chem. Soc. 139, 3209–3226 (2017).
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).
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).
Ritchie, T. J. & Macdonald, S. J. Physicochemical descriptors of aromatic character and their use in drug discovery. J. Med. Chem. 57, 7206–7215 (2014).
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).
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).
Tyler, D. S. et al. Click chemistry enables preclinical evaluation of targeted epigenetic therapies. Science 356, 1397–1401 (2017).
Dömling, A., Wang, W. & Wang, K. Chemistry and biology of multicomponent reactions. Chem. Rev. 112, 3083–3135 (2012).
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).
Liddle, J. et al. The discovery of GSK221149A: a potent and selective oxytocin antagonist. Bioorg. Med. Chem. Lett. 18, 90–94 (2008).
Zarganes-Tzitzikas, T. & Dömling, A. Modern multicomponent reactions for better drug syntheses. Org. Chem. Front. 1, 834–837 (2014).
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).
Arico-Muendel, C. C. From haystack to needle: finding value with DNA encoded library technology at GSK. MedChemComm 7, 1898–1909 (2016).
Satz, A. L. et al. DNA Compatible multistep synthesis and applications to DNA encoded libraries. Bioconj. Chem. 26, 1623–1632 (2015).
Thomas, B. et al. Application of biocatalysis to on-DNA carbohydrate library synthesis. Chembiochem 18, 858–863 (2017).
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).
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).
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).
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).
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).
Machutta, C. A. et al. Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening. Nat. Comm. 8, 16081 (2017).
Bollag, G. et al. Vemurafenib: the first drug approved for BRAF-mutant cancer. Nat. Rev. Drug Discov. 11, 873–886 (2012).
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).
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).
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).
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).
Morley, A. D. et al. Fragment-based hit identification: thinking in 3D. Drug Discov. Today 18, 1221–1227 (2013).
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).
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).
Ferenczy, G. G. & Keserü, G. M. How are fragments optimized? A retrospective analysis of 145 fragment optimizations. J. Med. Chem. 56, 2478–2486 (2013).
Kathman, S. G. & Statsyuk, A. V. Covalent tethering of fragments for covalent probe discovery. Med. Chem. Commun. 7, 576–585 (2016).
Backus, K. M. et al. Proteome-wide covalent ligand discovery in native biological systems. Nature 534, 570–574 (2016).
Parker, C. G. et al. Ligand and target discovery by fragment-based screening in human cells. Cell 168, 527–541 (2017).
Wetzel, S., Lachance, H. & Waldmann, H. in Comprehensive in Natural Products II (eds Mander, L. & Liu, H.-W.) 5–46 (Elsevier, 2010).
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).
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).
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).
Pascolutti, M. & Quinn, R. J. Natural products as lead structures: chemical transformations to create lead-like libraries. Drug Discov. Today 19, 215–221 (2014).
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).
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).
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).
Chow, S. Y. & Nelson, A. Embarking on a chemical space odyssey. J. Med. Chem. 60, 3591–3593 (2017).
Dow, M., Fisher, M., James, T., Marchetti, F. & Nelson, A. Towards the systematic exploration of chemical space. Org. Biomol. Chem. 10, 17–28 (2012).
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).
Doveston, R., Marsden, S. & Nelson, A. Towards the realisation of lead-oriented synthesis. Drug Discov. Today 19, 813–819 (2014).
Mayol-Llinàs, J., Nelson, A., Farnaby, W. & Ayscough, A. Assessing molecular scaffolds for CNS drug discovery. Drug Discov. Today 22, 965–969 (2017).
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).
Karageorgis, G., Warriner, S. & Nelson, A. Efficient discovery of bioactive scaffolds by activity-directed synthesis. Nat. Chem. 6, 872–876 (2014).
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).
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).
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).
Ardkhean, R. et al. Cascade polycyclizations in natural product synthesis. Chem. Soc. Rev. 45, 1557–1569 (2016).
Dückert, H. et al. Natural product-inspired cascade synthesis yields modulators of centrosome integrity. Nat. Chem. Biol. 8, 179 (2011).
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).
Davis, A. M., Plowright, A. T. & Valeur, E. Directing evolution: the next revolution in drug discovery? Nat. Rev. Drug Disc. 16, 681 (2017).
Shen, X., Corey, Chemistry, D. R. mechanism and clinical status of antisense oligonucleotides and duplex RNAs. Nucl. Ac. Res. 46, 1584–1600 (2018).
Lai, A. C. & Crews, C. M. Induced protein degradation: an emerging drug discovery paradigm. Nat. Rev. Drug Discov. 16, 101–114 (2017).
Sternbach, L. The benzodiazepine story. J. Med. Chem. 22, 1–7 (1979).
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).
Flam, F. The race to synthesize taxol ends in a tie. Science 263, 910–911 (1994).
Donehower, R. C. The clinical development of paclitaxel: a successful collaboration of academia, industry and the national cancer institute. Oncol. 1, 240–243 (1996).
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).
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).
Collins, K. D. & Glorius, F. A robustness screen for the rapid assessment of chemical reactions. Nat. Chem. 5, 597–601 (2013).
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).
Fleming, N. How artificial intelligence is changing drug discovery. Nature 557, S55–S57 (2018).
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).
Urquhart, L. Market watch: top drugs and companies by sales in 2017. Nat. Rev. Drug Discov. 17, 232 (2018).
Cernak, T. et al. Nanoscale synthesis and affinity ranking Nature 557, 228–232 (2018).
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).
Cole, K. P. et al. Kilogram-scale prexasertib monolactate monohydrate synthesis under continuous-flow CGMP conditions. Science 356, 1144–1150 (2017).
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).
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.
The authors declare competing interests: see Web version for details.
- 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.
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-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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