Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Counting on natural products for drug design

Nature Chemistry volume 8, pages 531–541 (2016)Cite this article

Subjects

Abstract

Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Rosuvastatin is a natural-product-inspired drug.
Figure 2: Natural products and their computationally generated fragments as inspiration for drug discovery.
Figure 3: Natural-product-inspired synthetic compounds with potent pharmacological effects.
Figure 4: Computer-assisted design of small molecules from natural product templates by scaffold simplification.
Figure 5: Fragment-based target identification with the SPiDER software.
Figure 6: Target predictions for natural products and synthetic bioactive molecules.

Similar content being viewed by others

References

  1. Lee, M. L. & Schneider, G. Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries. J. Comb. Chem. 3, 284–289 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Du, L. et al. Crowdsourcing natural products discovery to access uncharted dimensions of fungal metabolite diversity. Angew. Chem. Int. Ed. 53, 804–809 (2014).

    Article  CAS  Google Scholar 

  3. Bauer, A. & Brönstrup, M. Industrial natural product chemistry for drug discovery and development. Nat. Prod. Rep. 31, 35–60 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Brown, D. G., Lister, T. & May-Dracka, T. L. New natural products as new leads for antibacterial drug discovery. Bioorg. Med. Chem. Lett. 24, 413–418 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Scheepstra, M. et al. A natural-product switch for a dynamic protein interface. Angew. Chem. Int. Ed. 53, 6443–6448 (2014).

    Article  CAS  Google Scholar 

  6. Hattum, H. v. & Waldmann, H. Biology-oriented synthesis: harnessing the power of evolution. J. Am. Chem. Soc. 136, 11853–11859 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Harvey, A. L., Edrada-Ebel, R. & Quinn, R. J. The re-emergence of natural products for drug discovery in the genomics era. Nature Rev. Drug Discov. 14, 111–129 (2015).

    Article  CAS  Google Scholar 

  8. Koch, M. A. et al. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc. Natl Acad. Sci. USA 102, 17272–17277 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Eschenbrenner-Lux, V., Ku¨chler, P., Ziegler, S., Kumar, K. & Waldmann, H. An enantioselective inverse-electron-demand imino Diels-Alder reaction. Angew. Chem. Int. Ed. 53, 2134–2137 (2014).

    Article  CAS  Google Scholar 

  10. Zimmermann, T. J. et al. Biology-oriented synthesis of a tetrahydroisoquinoline-based compound collection targeting microtubule polymerization. ChemBioChem 14, 295–300 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Narayan, R., Bauer, J. O., Strohmann, C., Antonchick, A. P. & Waldmann, H. Catalytic enantioselective synthesis of functionalized tropanes reveals novel inhibitors of hedgehog signaling. Angew. Chem. Int. Ed. 52, 12892–12896 (2013).

    Article  CAS  Google Scholar 

  12. Lowe, D. B. Drug discovery: combichem all over again. Nature Chem. 6, 851–852 (2014).

    Article  CAS  Google Scholar 

  13. Molinari, G. Natural products in drug discovery: present status and perspectives. Adv. Exp. Med. Biol. 655, 13–27 (2009).

    Article  CAS  PubMed  Google Scholar 

  14. Koehn, F. E. & Carter, G. T. The evolving role of natural products in drug discovery. Nature Rev. Drug Discov. 4, 206–220 (2005).

    Article  CAS  Google Scholar 

  15. Harvey, A. L. Natural products in drug discovery. Drug Discov. Today 13, 894–901 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Patridge, E., Gareiss, P., Kinch, M. S. & Hoyer, D. An analysis of FDA-approved drugs: natural products and their derivatives. Drug Discov. Today 21, 204–207 (2015).

    Article  CAS  PubMed  Google Scholar 

  17. Basu, S. & Waldmann, H. Polymer supported synthesis of a natural product-inspired oxepane library. Bioorg. Med. Chem. 22, 4430–3333 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Huigens, R. W. III et al. A ring-distortion strategy to construct stereochemically complex and structurally diverse compounds from natural products. Nature Chem. 5, 195–202 (2013).

    Article  CAS  Google Scholar 

  19. Keller, N. P. Translating biosynthetic gene clusters into fungal armor and weaponry. Nature Chem. Biol. 11, 671–677 (2015).

    Article  CAS  Google Scholar 

  20. Medema, M. H. & Fischbach, M. A. Computational approaches to natural product discovery. Nature Chem. Biol. 11, 639–648 (2015).

    Article  CAS  Google Scholar 

  21. Kim, E., Moore, B. S. & Yoon, Y. J. Reinvigorating natural product combinatorial biosynthesis with synthetic biology. Nature Chem. Biol. 11, 649–659 (2015).

    Article  CAS  Google Scholar 

  22. Morrison, K. C. & Hergenrother, P. J. Natural products as starting points for the synthesis of complex and diverse compounds. Nat. Prod. Rep. 31, 6–14 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Henkel, T., Brunne, R. M., Müller, H. & Reichel, F. Statistical investigation into the structural complementarity of natural products and synthetic compounds. Angew. Chem. Int. Ed. 38, 643–647 (1999).

    Article  CAS  Google Scholar 

  24. Ertl, P., Jelfs, S., Muhlbacher, J., Schuffenhauer, A. & Selzer, P. Quest for the rings. In silico exploration of ring universe to identify novel bioactive heteroaromatic scaffolds. J. Med. Chem. 49, 4568–4573 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Akbulut, Y. et al. (−)-Englerin A is a potent and selective activator of TRPC4 and TRPC5 calcium channels. Angew. Chem. Int. Ed. 54, 3787–3791 (2015).

    Article  CAS  Google Scholar 

  26. Schuster, D., Laggner, C. & Langer, T. Why drugs fail — a study on side effects in new chemical entities. Curr. Pharm. Des. 11, 3545–3559 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. Hopkins, A. L., Keseru, G. M., Leeson, P. D., Rees, D. C. & Reynolds, C. H. The role of ligand efficiency metrics in drug discovery. Nature Rev. Drug Discov. 13, 105–121 (2014).

    Article  CAS  Google Scholar 

  28. Murray, C. W. & Rees, D. C. The rise of fragment-based drug discovery. Nature Chem. 1, 187–192 (2009).

    Article  CAS  Google Scholar 

  29. Grabowski, K., Baringhaus, K. H. & Schneider, G. Scaffold diversity of natural products: inspiration for combinatorial library design. Nat. Prod. Rep. 25, 892–904 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Barelier, S. et al. Increasing chemical space coverage by combining empirical and computational fragment screens. ACS Chem. Biol. 9, 1528–1535 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Reker, D. et al. Revealing the macromolecular targets of complex natural products. Nature Chem. 6, 1072–1078 (2014).

    Article  CAS  Google Scholar 

  32. Reutlinger, M., Rodrigues, T., Schneider, P. & Schneider, G. Multi-objective molecular de novo design by adaptive fragment prioritization. Angew. Chem. Int. Ed. 53, 4244–4248 (2014).

    Article  CAS  Google Scholar 

  33. Parkinson, E. I. et al. Deoxynybomycins inhibit mutant DNA gyrase and rescue mice infected with fluoroquinolone-resistant bacteria. Nature Commun. 6, 6947 (2015).

    Article  CAS  Google Scholar 

  34. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Meth. 44, 235–249 (2000).

    Article  CAS  Google Scholar 

  35. Congreve, M., Carr, R., Murray, C. & Jhoti, H. A rule of three for fragment-based lead discovery? Drug Discov. Today 8, 876–877 (2003).

    Article  PubMed  Google Scholar 

  36. Baell, J., Congreve, M., Leeson, P. & Abad-Zapatero, C. Ask the experts: past, present and future of the rule of five. Future Med. Chem. 5, 745–752 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Ntie-Kang, F., Lifongo, L. L., Judson, P. N., Sippl, W. & Efange, S. M. How “drug-like” are naturally occurring anti-cancer compounds? J. Mol. Model. 20, 2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Kenny, P. W. & Montanari, C. A. Inflation of correlation in the pursuit of drug-likeness. J. Comput. Aided Mol. Des. 27, 1–13 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Johnstone, C. Medicinal chemistry matters — a call for discipline in our discipline. Drug Discov. Today 17, 538–543 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Quinn, R. J. et al. Developing a druglike natural product library. J. Nat. Prod. 71, 464–468 (2008).

    Article  CAS  PubMed  Google Scholar 

  41. Antonchick, A. P. et al. Highly enantioselective synthesis and cellular evaluation of spirooxindoles inspired by natural products. Nature Chem. 2, 735–740 (2010).

    Article  CAS  Google Scholar 

  42. Svenda, J. et al. Biology-oriented synthesis of a withanolide-inspired compound collection reveals novel modulators of hedgehog signalling. Angew. Chem. Int. Ed. 54, 5596–5602 (2015).

    Article  CAS  Google Scholar 

  43. Schmid, F., Jessen, H. J., Burch, P. & Gademann, K. Truncated militarinone fragments identified by total chemical synthesis induce neurite outgrowth. Med. Chem. Comm. 4, 135–139 (2013).

    Article  CAS  Google Scholar 

  44. DeLorbe, J. E., Clements, J. H., Whiddon, B. B. & Martin, S. F. Thermodynamic and structural effects of macrocyclic constraints in protein−ligand interactions. ACS Med. Chem. Lett. 1, 448–452 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Giordanetto, F. & Kihlberg, J. Macrocyclic drugs and clinical candidates: What can medicinal chemists learn from their properties? J. Med. Chem. 57, 278–295 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Zhang, J. et al. Diversity-oriented synthesis of Lycopodium alkaloids inspired by the hidden functional group pairing pattern. Nature Commun. 5, 4614 (2014).

    Article  CAS  Google Scholar 

  47. Beckmann, H. S. et al. A strategy for the diversity-oriented synthesis of macrocyclic scaffolds using multidimensional coupling. Nature Chem. 5, 861–867 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  49. McLeod, M. C. et al. Probing chemical space with alkaloid-inspired libraries. Nature Chem. 6, 133–140 (2014).

    Article  CAS  Google Scholar 

  50. Elumalai, N., Berg, A., Natarajan, K., Scharow, A. & Berg, T. Nanomolar inhibitors of the transcription factor STAT5b with high selectivity over STAT5a. Angew. Chem. Int. Ed. 54, 4756–4763 (2015).

    Article  CAS  Google Scholar 

  51. Larsson, J., Gottfries, J., Muresan, S. & Backlund, A. ChemGPS-NP: tuned for navigation in biologically relevant chemical space. J. Nat. Prod. 70, 789–794 (2007).

    Article  CAS  PubMed  Google Scholar 

  52. Miyao, T., Reker, D., Schneider, P., Funatsu, K. & Schneider, G. Chemography of natural product space. Planta Med. 81, 429–435 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Reutlinger, M. & Schneider, G. Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. J. Mol. Graph. Model. 34, 108–117 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Bon, R. S. & Waldmann, H. Bioactivity-guided navigation of chemical space. Acc. Chem. Res. 43, 1103–1114 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Lachance, H., Wetzel, S., Kumar, K. & Waldman, H. Charting, navigating, and populating natural product chemical space for drug discovery. J. Med. Chem. 55, 5989–6001 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Takayama, H. et al. Discovery of inhibitors of the Wnt and Hedgehog signaling pathways through the catalytic enantioselective synthesis of an iridoid-inspired compound collection. Angew. Chem. Int. Ed. 52, 12404–12408 (2013).

    Article  CAS  Google Scholar 

  57. Dakas, P. Y. et al. Discovery of neuritogenic compound classes inspired by natural products. Angew. Chem. Int. Ed. 52, 9576–9581 (2013).

    Article  CAS  Google Scholar 

  58. Voigt, T. et al. A natural product inspired tetrahydropyran collection yields mitosis modulators that synergistically target CSE1L and tubulin. Angew. Chem. Int. Ed. 52, 410–414 (2013).

    Article  CAS  Google Scholar 

  59. Bennani, Y. L. Drug discovery in the next decade: innovation needed ASAP. Drug Discov. Today 16, 779–792 (2011).

    Article  PubMed  Google Scholar 

  60. Wetzel, S. et al. Interactive exploration of chemical space with Scaffold Hunter. Nature Chem. Biol. 5, 581–583 (2009).

    Article  CAS  Google Scholar 

  61. Renner, S. et al. Bioactivity-guided mapping and navigation of chemical space. Nature Chem. Biol. 5, 585–592 (2009).

    Article  CAS  Google Scholar 

  62. Over, B. et al. Natural-product-derived fragments for fragment-based ligand discovery. Nature Chem. 5, 21–28 (2013).

    Article  CAS  Google Scholar 

  63. Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nature Chem. 4, 90–98 (2012).

    Article  CAS  Google Scholar 

  64. Nickel, J. et al. SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 42, W26–W31 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Keiser, M. J. et al. Relating protein pharmacology by ligand chemistry. Nature Biotechnol. 25, 197–206 (2007).

    Article  CAS  Google Scholar 

  67. Gfeller, D. et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 42, W32–W38 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Article  CAS  Google Scholar 

  69. Antolin, A. A., Jalencas, X., Yelamos, J. & Mestres, J. Identification of pim kinases as novel targets for PJ34 with confounding effects in PARP biology. ACS Chem. Biol. 7, 1962–1967 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chem. Biol. 4, 682–690 (2008).

    Article  CAS  Google Scholar 

  71. Lounkine, E. et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361–367 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Rollinger, J. M. Accessing target information by virtual parallel screening — the impact on natural product research. Phytochem. Lett. 2, 53–58 (2009).

    Article  CAS  Google Scholar 

  73. Do, Q. T. et al. Reverse pharmacognosy: identifying biological properties for plants by means of their molecule constituents: application to meranzin. Planta Med. 73, 1235–1240 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. Vuorinen, A., Nashev, L. G., Odermatt, A., Rollinger, J. M. & Schuster, D. Pharmacophore model refinement for 11β-hydroxysteroid dehydrogenase inhibitors: search for modulators of intracellular glucocorticoid concentrations. Mol. Inf. 33, 15–25 (2014).

    Article  CAS  Google Scholar 

  75. Bauer, J. et al. Discovery of depsides and depsidones from lichen as potent inhibitors of microsomal prostaglandin E2 synthase-1 using pharmacophore models. ChemMedChem 7, 2077–2081 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Rollinger, J. M. et al. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med. 75, 195–204 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. Schuster, D. et al. Morphinans and isoquinolines: acetylcholinesterase inhibition, pharmacophore modeling, and interaction with opioid receptors. Bioorg. Med. Chem. 18, 5071–5080 (2010).

    Article  CAS  PubMed  Google Scholar 

  78. Fakhrudin, N. et al. Computer-aided discovery, validation, and mechanistic characterization of novel neolignan activators of peroxisome proliferator-activated receptor gamma. Mol. Pharmacol. 77, 559–566 (2010).

    Article  CAS  PubMed  Google Scholar 

  79. Atanasov, A. G. et al. Honokiol: a non-adipogenic PPARγ agonist from nature. Biochim. Biophys. Acta 1830, 4813–4819 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nature Chem. Biol. 11, 536–541 (2015).

    Article  CAS  Google Scholar 

  81. Lagunin, A., Stepanchikova, A., Filimonov, D. & Poroikov, V. PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16, 747–748 (2000).

    Article  CAS  PubMed  Google Scholar 

  82. Lagunin, A., Filimonov, D. & Poroikov, V. Multi-targeted natural products evaluation based on biological activity prediction with PASS. Curr. Pharm. Des. 16, 1703–1717 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. Filimonov, D., Poroikov, V., Borodina, Y. & Gloriozova, T. Chemical similarity assessment through multilevel neighbourhoods of atoms: definition and comparison with the other descriptors. J. Chem. Inf. Comput. Sci. 39, 666–670 (1999).

    Article  CAS  Google Scholar 

  84. Chen, X. et al. Drug–target interaction prediction: databases, web servers and computational models. Brief. Bioinform. http://dx.doi.org/10.1093/bib/bbv066 (2015).

  85. Liu, X. et al. Predicting targeted polypharmacology for drug repositioning and multi-target drug discovery. Curr. Med. Chem. 20, 1646–1661 (2013).

    Article  CAS  PubMed  Google Scholar 

  86. Pahikkala, T. et al. Toward more realistic drug–target interaction predictions. Brief. Bioinform. 16, 325–337 (2015).

    Article  CAS  PubMed  Google Scholar 

  87. Mousavian, Z. & Masoudi-Nejad, A. Drug–target interaction prediction via chemogenomic space: learning-based methods. Expert Opin. Drug Metab. Toxicol. 10, 1273–1287 (2014).

    Article  PubMed  Google Scholar 

  88. Yamanishi, Y. Chemogenomic approaches to infer drug-target interaction networks. Meth. Mol. Biol. 939, 97–113 (2013).

    Article  CAS  Google Scholar 

  89. Rupp, M. et al. Machine learning estimates of natural product conformational energies. PLoS Comput. Biol. 10, e1003400 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Reker, D., Rodrigues, T., Schneider, P. & Schneider, G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc. Natl Acad. Sci. USA 111, 4067–4072 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Schneider, G., Reker, D., Rodrigues, T. & Schneider, P. Coping with polypharmacology by computational medicinal chemistry. Chimia 68, 648–653 (2014).

    Article  CAS  PubMed  Google Scholar 

  92. Reutlinger, M. et al. Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for 'orphan' molecules. Mol. Inf. 32, 133–138 (2013).

    Article  CAS  Google Scholar 

  93. Reker, D. et al. Deorphaning pyrrolopyrazines as potent multi-target antimalarial agents. Angew. Chem. Int. Ed. 53, 7079–7084 (2014).

    Article  CAS  Google Scholar 

  94. Rodrigues, T., Reker, D., Kunze, J., Schneider, P. & Schneider, G. Revealing the macromolecular targets of fragment-like natural products. Angew. Chem. Int. Ed. 54, 10516–10520 (2015).

    Article  CAS  Google Scholar 

  95. Imming, P. Molecular targets of natural drug substances: idiosyncrasies and preferences. Planta Med. 76, 1794–1801 (2010).

    Article  CAS  PubMed  Google Scholar 

  96. Koeberle, A. & Werz, O. Multi-target approach for natural products in inflammation. Drug Discov. Today 19, 1871–1882 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Geldenhuys, W. J. & Van der Schyf, C. J. Rationally designed multi-targeted agents against neurodegenerative diseases. Curr. Med. Chem. 20, 1662–1672 (2013).

    Article  CAS  PubMed  Google Scholar 

  98. Schmeller, T., Sauerwein, M., Sporer, F., Wink, M. & Muller, W. E. Binding of quinolizidine alkaloids to nicotinic and muscarinic acetylcholine receptors. J. Nat. Prod. 57, 1316–1319 (1994).

    Article  CAS  PubMed  Google Scholar 

  99. Kirchmair, J. et al. Predicting drug metabolism: experiment and/or computation? Nature Rev. Drug Discov. 14, 387–404 (2015).

    Article  CAS  Google Scholar 

  100. Gertsch, J. Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures. Planta Med. 77, 1086–1098 (2011).

    Article  CAS  PubMed  Google Scholar 

  101. Mak, L. et al. Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism. Curr. Pharm. Des. 19, 532–577 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. de Sá Alves, F. R., Barreiro, E. J. & Fraga, C. A. From nature to drug discovery: the indole scaffold as a 'privileged structure'. Mini Rev. Med. Chem. 9, 782–793 (2009).

    Article  PubMed  Google Scholar 

  103. Venkatraman, P. Specificity in the interaction of natural products with their target proteins — a biochemical and structural insight. Mini Rev. Med. Chem. 10, 540–549 (2010).

    Article  CAS  PubMed  Google Scholar 

  104. Mishra, B. B. & Tiwari, V. K. Natural products: an evolving role in future drug discovery. Eur. J. Med. Chem. 46, 4769–4807 (2011).

    Article  CAS  PubMed  Google Scholar 

  105. Jayaseelan, K. V., Moreno, P., Truszkowski, A., Ertl, P. & Steinbeck, C. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinform. 13, 106 (2012).

    Article  Google Scholar 

  106. Roche, O. et al. Development of a virtual screening method for identification of “frequent hitters” in compound libraries. J. Med. Chem. 45, 137–142 (2002).

    Article  CAS  PubMed  Google Scholar 

  107. Coan, K. E. & Shoichet, B. K. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J. Am. Chem. Soc. 130, 9606–9612 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Seidler, J., McGovern, S. L., Doman, T. N. & Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 4477–4486 (2003).

    Article  CAS  PubMed  Google Scholar 

  109. Baell, J. & Walters, M. A. Chemistry: chemical con artists foil drug discovery. Nature 513, 481–483 (2014).

    Article  CAS  PubMed  Google Scholar 

  110. Petrova, T., Chuprina, A., Parkesh, R. & Pushechnikov, A. Structural enrichment of HTS compounds from available commercial libraries. Med. Chem. Comm. 3, 571–579 (2012).

    Article  CAS  Google Scholar 

  111. Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).

    Article  CAS  Google Scholar 

  112. Duan, D., Doak, A. K., Nedyalkova, L. & Shoichet, B. K. Colloidal aggregation and the in vitro activity of traditional Chinese medicines. ACS Chem. Biol. 10, 978–988 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Wishart, D. S. et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901–D906 (2008).

    Article  CAS  PubMed  Google Scholar 

  114. Bento, A. P. et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, D1083–D1090 (2014).

    Article  CAS  PubMed  Google Scholar 

  115. Chen, C. Y. TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico. PLoS ONE 6, e15939 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Rishton, G. M. Reactive compounds and in vitro false positives in HTS. Drug Discov. Today 2, 382–384 (1997).

    Article  CAS  Google Scholar 

  117. Hann, M. et al. Strategic pooling of compounds for high-throughput screening. J. Chem. Inf. Comput. Sci. 39, 897–902 (1999).

    Article  CAS  PubMed  Google Scholar 

  118. Baell, J. B. & Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719–2740 (2010).

    Article  CAS  PubMed  Google Scholar 

  119. Lagorce, D., Sperandio, O., Baell, J. B., Miteva, M. A. & Villoutreix, B. O. FAF-Drugs3: a web server for compound property calculation and chemical library design. Nucleic Acids Res. 43, W200–W207 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Jost, C., Nitsche, C., Scholz, T., Roux, L. & Klein, C. D. Promiscuity and selectivity in covalent enzyme inhibition: a systematic study of electrophilic fragments. J. Med. Chem. 57, 7590–7599 (2014).

    Article  CAS  PubMed  Google Scholar 

  121. Schneider, P., Röthlisberger, M., Reker, D. & Schneider, G. Spotting and designing promiscuous ligands for drug discovery. Chem. Commun. 52, 1135–1138 (2016).

    Article  CAS  Google Scholar 

  122. Clemons, P. A. et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc. Natl Acad. Sci. USA 107, 18787–18792 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Kell, D. B. Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it. FEBS J. 280, 5957–5980 (2013).

    Article  CAS  PubMed  Google Scholar 

  124. Rollinger, J. M., Stuppner, H. & Langer, T. Virtual screening for the discovery of bioactive natural products. Prog. Drug Res. 65, 213–249 (2008).

    Google Scholar 

  125. Harvey, A. L., Edrada-Ebel, R. & Quinn, R. J. The re-emergence of natural products for drug discovery in the genomics era. Nature Rev. Drug Discov. 14, 111–129 (2015).

    Article  CAS  Google Scholar 

  126. Cragg, G. M., Grothaus, P. G. & Newman, D. J. New horizons for old drugs and drug leads. J. Nat. Prod. 77, 703–723 (2014).

    Article  CAS  PubMed  Google Scholar 

  127. Schuster, D. & Wolber, G. Identification of bioactive natural products by pharmacophore-based virtual screening. Curr. Pharm. Des. 16, 1666–1681 (2010).

    Article  CAS  PubMed  Google Scholar 

  128. Jacoby, E. & Mozzarelli, A. Chemogenomic strategies to expand the bioactive chemical space. Curr. Med. Chem. 16, 4374–4381 (2009).

    Article  CAS  PubMed  Google Scholar 

  129. Hübel, K., Lessmannm, T. & Waldmann, H. Chemical biology — identification of small molecule modulators of cellular activity by natural product inspired synthesis. Chem. Soc. Rev. 37, 1361–1374 (2008).

    Article  CAS  PubMed  Google Scholar 

  130. Kumar, K. & Waldmann, H. Synthesis of natural product inspired compound collections. Angew. Chem. Int. Ed. 48, 3224–3242 (2009).

    Article  CAS  Google Scholar 

  131. Hirai, G. Mimicking/extracting structure and functions of natural products: synthetic approaches that address unexplored needs in chemical biology. Chem. Rec. 15, 445–456 (2015).

    Article  CAS  PubMed  Google Scholar 

  132. Thomas, G. L. & Johannes, C. W. Natural product-like synthetic libraries. Curr. Opin. Chem. Biol. 15, 516–522 (2011).

    Article  CAS  PubMed  Google Scholar 

  133. Gupta, P. K., Barone, G., Gurley, B. J., Fifer, E. K. & Hendrickson, H. P. Hydrastine pharmacokinetics and metabolism after a single oral dose of goldenseal (Hydrastis canadensis) to humans. Drug Metab. Dispos. 43, 534–552 (2015).

    Article  CAS  PubMed  Google Scholar 

  134. Bier, D., Thiel, P., Briels, J. & Ottmann, C. Stabilization of protein–protein interactions in chemical biology and drug discovery. Prog. Biophys. Mol. Biol. 119, 10–19 (2015).

    Article  CAS  PubMed  Google Scholar 

  135. Klenner, A., Hartenfeller, M., Schneider, P. & Schneider, G. 'Fuzziness' in pharmacophore-based virtual screening and de novo design. Drug Discov. Today Technol. 7, e237–e244 (2010).

    Article  CAS  Google Scholar 

  136. 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).

    Article  CAS  PubMed  Google Scholar 

  137. Kusari, S. et al. Tramadol — a true natural product? Angew. Chem. Int. Ed. Engl. 53, 12073–12076 (2014).

    Article  CAS  PubMed  Google Scholar 

  138. Talalay, P., De Long, M. J. & Prochaska, H. J. Identification of a common chemical signal regulating the induction of enzymes that protect against chemical carcinogenesis. Proc. Natl Acad. Sci. USA 85, 8261–8265 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Aptula, A. O. & Roberts, D. W. Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: general principles and application to reactive toxicity. Chem. Res. Toxicol. 19, 1097–1105 (2006).

    Article  CAS  PubMed  Google Scholar 

  140. Amslinger, S. The tunable functionality of α,β-unsaturated carbonyl compounds enables their differential application in biological systems. ChemMedChem 5, 351–356 (2010).

    Article  CAS  PubMed  Google Scholar 

  141. Avonto, C. et al. An NMR spectroscopic method to identify and classify thiol-trapping agents: revival of Michael acceptors for drug discovery? Angew. Chem. Int. Ed. 50, 467–471 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank K.-H. Altmann for invaluable advice and discussion. This study was financially supported by the OPO Foundation, Zürich.

Author information

Authors and Affiliations

  1. Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Institute of Pharmaceutical Sciences, Vladimir-Prelog-Weg 4, Zürich, 8093, Switzerland

    Tiago Rodrigues, Daniel Reker, Petra Schneider & Gisbert Schneider

  2. inSili.com LLC, Segantinisteig 3, Zürich, 8049, Switzerland

    Petra Schneider

Authors
  1. Tiago Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Reker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Petra Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gisbert Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally.

Corresponding author

Correspondence to Gisbert Schneider.

Ethics declarations

Competing interests

P.S. and G.S. are the founders of inSili.com LLC, Zürich. All the other authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodrigues, T., Reker, D., Schneider, P. et al. Counting on natural products for drug design. Nature Chem 8, 531–541 (2016). https://doi.org/10.1038/nchem.2479

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchem.2479

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing