Skip to main content

Polypharmacology and Natural Products

  • Chapter
  • First Online:
Polypharmacology

Abstract

Natural products (NPs) play an important role in drug discovery. No less than 50% of the Food and Drug Administration (FDA)-approved drugs were NPs or NPs derivatives. More importantly, NPs have become an indispensable component of polypharmacology and a constant source of bioactive compounds or a golden mine of multitarget drugs (MTD drugs) because of their multitargeting property: recent studies have unequivocally demonstrated that FDA-approved, clinically tested, and experimentally investigational NPs commonly act on multiple molecular targets (e.g., proteins, DNAs, RNAs). With diverse chemical structures, NPs and their derivatives contribute greatly to the landscape of new chemical entities (NCEs) for de novo drug discovery and development, as well as drug repositioning, in the past, at present, and in the future. This chapter will first explain the relationship between NPs and polypharmacology, which will be followed by an introduction to the relationships between systems pharmacology/network pharmacology and NPs polypharmacology. Then, the current applications of NPs as MTDs to cancer therapy will be summarized. Finally, resources and tools for exploiting NPs polypharmacology will be discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod. 2012;75:311–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ji HF, Li XJ, Zhang HY. Natural products and drug discovery: can thousands of years of ancient medical knowledge lead us to new and powerful drug combinations in the fight against cancer and dementia? EMBO Rep. 2009;10:194–200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Harvey A. Natural products in drug discovery. Drug Discov Today. 2008;13:894–901.

    Article  CAS  PubMed  Google Scholar 

  4. Butler MS, Robertson AA, Cooper MA. Natural product and natural product derived drugs in clinical trials. Nat Prod Rep. 2014;31:1612–61.

    Article  CAS  PubMed  Google Scholar 

  5. Shen B. A new golden age of natural products drug discovery. Cell. 2015;163:1297–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kingston DGI. Modern natural products drug discovery and its relevance to biodiversity conservation. J Nat Prod. 2011;74:496–511.

    Article  CAS  PubMed  Google Scholar 

  7. Chin YW, Balunas MJ, Chai HB, et al. Drug discovery from natural sources. AAPS J. 2006;8:E239–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ho TT, Tran QT, Chai CL. The polypharmacology of natural products. Future Med Chem. 2018;10(11):1361–8.

    Article  CAS  PubMed  Google Scholar 

  9. Fang J, Liu C, Wang Q, et al. In silico polypharmacology of natural products. Brief Bioinform. 2018;19(6):1153–71.

    CAS  PubMed  Google Scholar 

  10. Gu J, Gui Y, Chen L, et al. Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One. 2013;8(4):e62839.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kibble M, Saarinen N, Tang J, et al. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep. 2015;32(8):1249–66.

    Article  CAS  PubMed  Google Scholar 

  12. Yildirim MA, Goh KI, Cusick ME, et al. Drug-target network. Nat. Biotech. 2007;25:1119–26.

    CAS  Google Scholar 

  13. Barneh F, Jafari M, Mirzaie M. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief Bioinform. 2016;17:1070–80.

    CAS  PubMed  Google Scholar 

  14. DeCorte BL. Underexplored opportunities for natural products in drug discovery. J Med Chem. 2016;59:9295–304.

    Article  CAS  PubMed  Google Scholar 

  15. Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov. 2015;14:111–29.

    Article  CAS  PubMed  Google Scholar 

  16. Li JW, Vederas JC. Drug discovery and natural products: end of an era or an endless frontier? Science. 2009;325:161–5.

    Article  PubMed  CAS  Google Scholar 

  17. Fang J, Cai C, Wang Q, et al. Systems pharmacology-based discovery of natural products for precision oncology through targeting cancer mutated genes. CPT Pharmacometrics Syst Pharmacol. 2017;6:177–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Clardy J, Walsh C. Lessons from natural molecules. Nature. 2004;432:829–37.

    Article  CAS  PubMed  Google Scholar 

  19. Gu J, Zhang H, Chen L, et al. Drug-target network and polypharmacology studies of a Traditional Chinese Medicine for type II diabetes mellitus. Computational Biol Chem. 2011;35:293–7.

    Article  CAS  Google Scholar 

  20. Vogt I, Mestres J. Drug-target networks Mol Informatics. 2010;29:10–4.

    CAS  Google Scholar 

  21. Mayr F, Möller G, Garscha U, et al. Finding new molecular targets of familiar natural products using in silico target prediction. Int J Mol Sci. 2020;21(19):7102.

    Article  CAS  PubMed Central  Google Scholar 

  22. Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod. 2016;79:629–61.

    Article  CAS  PubMed  Google Scholar 

  23. Koehn FE, Carter GT. The evolving role of natural products in drug discovery. Nat Rev Drug Discov. 2005;4:206–20.

    Article  CAS  PubMed  Google Scholar 

  24. Koeberle A, Werz O. Multi-target approach for natural products in inflammation. Drug Discov Today. 2014;19:1871–82.

    Article  CAS  PubMed  Google Scholar 

  25. Rodrigues T, Reker D, Schneider P, et al. Counting on natural products for drug design. Nat Chem. 2016;8:531.

    Article  CAS  PubMed  Google Scholar 

  26. Clemons PA, Bodycombe NE, Carrinski HA, et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc Natl Acad Sci U S A. 2010;107:18787.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang XJ, Wei XM, Thijssen B, et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotech. 2012;30:159–64.

    Article  CAS  Google Scholar 

  28. Yang K, Ma WZ, Liang HH, et al. Dynamic simulations on the arachidonic acid metabolic network. PloS Computational Biol. 2007;3:523–30.

    CAS  Google Scholar 

  29. Zhang Y, Thiele I, Weekes D, et al. Three-dimensional structural view of the central metabolic network of thermotoga maritima. Science. 2009;325:1544–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li QA, Li XD, Li CH, et al. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds. PLoS One. 2011;6(3):e14774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hong JY. Role of natural product diversity in chemical biology. Curr Opinion Chem Biol. 2011;15:350–4.

    Article  CAS  Google Scholar 

  32. Firn RD, Jones CG. Natural products – a simple model to explain chemical diversity. Nat Prod Reports. 2003;20:382–91.

    Article  CAS  Google Scholar 

  33. Basso LA, da Silva LHP, Fett-Neto AG, et al. The use of biodiversity as source of new chemical entities against defined molecular targets for treatment of malaria, tuberculosis, and T-cell mediated diseases – a review. Memorias Do Instituto Oswaldo Cruz. 2005;100:575–606.

    Article  Google Scholar 

  34. Quinn RJ, Carroll AR, Pham NB, et al. Developing a drug-like natural product library. J Nat Prod. 2008;71:464–8.

    Article  CAS  PubMed  Google Scholar 

  35. Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Info Computer Sci. 2003;43:218–27.

    Article  CAS  Google Scholar 

  36. Yongye AB, Waddell J, Medina-Franco JL. Molecular scaffold analysis of natural products databases in the public domain. Chem Biol & Drug Design. 2012;80:717–24.

    Article  CAS  Google Scholar 

  37. Grabowski K, Baringhaus KH, Schneider G. Scaffold diversity of natural products: inspiration for combinatorial library design. Nat Prod Reports. 2008;25:892–904.

    Article  CAS  Google Scholar 

  38. Lee ML, 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. 2001;3:284–9.

    Article  CAS  PubMed  Google Scholar 

  39. Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.

    Article  CAS  PubMed  Google Scholar 

  40. Rosen J, Gottfries J, Muresan S, et al. Novel chemical space exploration via natural products. J Medicinal Chem. 2009;52:1953–62.

    Article  CAS  Google Scholar 

  41. Grabowski K, Schneider G. Properties and architecture of drugs and natural products revisited. Curr Chem Biol. 2007;1:115–27.

    CAS  Google Scholar 

  42. Henkel T, Brunne RM, Muller H, et al. Statistical investigation into the structural complementarity of natural products and synthetic compounds. Angewandte Chemie-International Edition. 1999;38:643–7.

    Article  CAS  PubMed  Google Scholar 

  43. Qiao XB, Hou TJ, Zhang W, et al. A 3D structure database of components from Chinese traditional medicinal herbs. J Chem Inf Comput Sci. 2002;42:481–9.

    Article  CAS  PubMed  Google Scholar 

  44. Doman TN, McGovern SL, Witherbee BJ, et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Medicinal Chem. 2002;45:2213–21.

    Article  CAS  Google Scholar 

  45. Zhu F, Shi Z, Qin C, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucl Acids Res. 2012;40:D1128–36.

    Article  CAS  PubMed  Google Scholar 

  46. Fang J, Wu Z, Cai C, et al. Quantitative and systems pharmacology. 1. In silico prediction of drug-target interactions of natural products enables new targeted cancer therapy. J Chem Inf Model. 2017;57(11):2657–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Luo H, Mattes W, Mendrick DL, et al. Molecular docking for identification of potential targets for drug repurposing. Curr Top Med Chem. 2016;16:3636–45.

    Article  CAS  PubMed  Google Scholar 

  48. Ye H, Wei J, Tang K, et al. Drug repositioning through network pharmacology. Curr Top Med Chem. 2016;16:3646–56.

    Article  CAS  PubMed  Google Scholar 

  49. Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8:e1002503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Cheng F, Zhou Y, Li W, et al. Prediction of chemical-protein interactions network with weighted network-based inference method. PLoS One. 2012;7:e41064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Li J, Lei K, Wu Z, et al. Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget. 2016;7:45584–96.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Li J, Wu Z, Cheng F, et al. Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep. 2014;4:5576.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wu Z, Cheng F, Li J, et al. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform. 2017;18:333–47.

    CAS  PubMed  Google Scholar 

  54. Wu Z, Lu W, Wu D, et al. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol. 2016;173:3372–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cheng F, Li W, Wang X, et al. Adverse drug events: database construction and in silico prediction. J Chem Inf Model. 2013;53:744–52.

    Article  CAS  PubMed  Google Scholar 

  56. Cheng F, Li W, Wu Z, et al. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. J Chem Inf Model. 2013;53:753–62.

    Article  CAS  PubMed  Google Scholar 

  57. Lu W, Cheng F, Jiang J, et al. FXR Antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach. Sci Rep. 2015;5:8114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Campbell IB, Macdonald SJF, Procopiou PA. Medicinal chemistry in drug discovery in big pharma: past, present and future. Drug Discov Today. 2018;23:219–34.

    Article  PubMed  Google Scholar 

  59. Chen C, Huang H, Wu CH. Protein bioinformatics databases and resources. In: Chen C, Huang H, Wu CH, editors. Fundamentals of protein bioinformatics, vol. 1558. New York: Humana Press; 2017. p. 3–39.

    Chapter  Google Scholar 

  60. Sliwoski G, Kothiwale S, Meiler J, et al. Computational methods in drug discovery. Pharmacol Rev. 2014;66:334–95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673–83.

    Article  CAS  PubMed  Google Scholar 

  62. Aronson JK. Old drugs—new uses. Br J Clin Pharmacol. 2007;64:563–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hurle MR, Yang L, Xie Q, et al. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013;93:335–41.

    Article  CAS  PubMed  Google Scholar 

  64. Cereto-Massagué A, Ojeda MJ, Valls C, et al. Tools for in silico target fishing. Methods. 2015;71:98–103.

    Article  PubMed  CAS  Google Scholar 

  65. Huang Y-W, Pineau I, Chang H-J, et al. Critical residues for the specificity of cofactors and substrates in human estrogenic 17β-hydroxysteroid dehydrogenase 1: variants designed from the three-dimensional structure of the enzyme. Mol Endocrinol. 2001;15:2010–20.

    CAS  PubMed  Google Scholar 

  66. Sydow D, Burggraaff L, Szengel A, et al. Advances and challenges in computational target prediction. J Chem Inf Model. 2019;59:1728–42.

    Article  CAS  PubMed  Google Scholar 

  67. Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2018;47:D1102–9.

    Article  PubMed Central  Google Scholar 

  68. Casey SC, Amedei A, Aquilano K, et al. Cancer prevention and therapy through the modulation of the tumor microenvironment. Semin Cancer Biol. 2015;35:S199–223.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Liu Y, Cao X. Immunosuppressive cells in tumor immune escape and metastasis. J Mol Med (Berl). 2016;94:509–22.

    Article  CAS  Google Scholar 

  70. Kotecha R, Takami A, Espinoza JL. Dietary phytochemicals and cancer chemoprevention: a review of the clinical evidence. Oncotarget. 2016;7:52517–29.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Jantan I, Ahmad W, Bukhari SN. Plant-derived immunomodulators: an insight on their preclinical evaluation and clinical trials. Front Plant Sci. 2015;6:655.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Calderon-Montano JM, Burgos-Moron E, Perez-Guerrero C, et al. A review on the dietary flavonoid kaempferol. Mini Rev Med Chem. 2011;11:298–344.

    Article  CAS  PubMed  Google Scholar 

  73. Kim SH, Hwang KA, Choi KC. Treatment with kaempferol suppresses breast cancer cell growth caused by estrogen and triclosan in cellular and xenograft breast cancer models. J Nutr Biochem. 2016;28:70–82.

    Article  CAS  PubMed  Google Scholar 

  74. Luo H, Rankin GO, Liu L, et al. Kaempferol inhibits angiogenesis and VEGF expression through both HIF dependent and independent pathways in human ovarian cancer cells. Nutr Cancer. 2009;61:554–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Goettert M, Schattel V, Koch P, et al. Biological evaluation and structural determinants of p38alpha mitogen-activated-protein kinase and c-Jun-N-terminal kinase 3 inhibition by flavonoids. Chembiochem. 2010;11:2579–88.

    Article  CAS  PubMed  Google Scholar 

  76. Arai Y, Endo S, Miyagi N, et al. Structure-activity relationship of flavonoids as potent inhibitors of carbonyl reductase 1 (CBR1). Fitoterapia. 2015;101:51–6.

    Article  CAS  PubMed  Google Scholar 

  77. Kasi PD, Tamilselvam R, Skalicka-Wozniak K, et al. Molecular targets of curcumin for cancer therapy: an updated review. Tumour Biol. 2016;37:13017–28.

    Article  CAS  PubMed  Google Scholar 

  78. Bar-Sela G, Epelbaum R, Schaffer M. Curcumin as an anticancer agent: review of the gap between basic and clinical applications. Curr Med Chem. 2010;17:190–7.

    Article  CAS  PubMed  Google Scholar 

  79. Chakraborty G, Jain S, Kale S, et al. Curcumin suppresses breast tumor angiogenesis by abrogating osteopontin-induced VEGF expression. Mol Med Rep. 2008;1:641–6.

    CAS  PubMed  Google Scholar 

  80. Bhaumik S, Jyothi MD, Khar A. Differential modulation of nitric oxide production by curcumin in host macrophages and NK cells. FEBS Lett. 2000;483:78–82.

    Article  CAS  PubMed  Google Scholar 

  81. Surh YJ, Chun KS, Cha HH, et al. Molecular mechanisms underlying chemopreventive activities of anti-inflammatory phytochemicals: down-regulation of COX-2 and iNOS through suppression of NF-kappa B activation. Mutat Res. 2001;480–481:243–68.

    Article  PubMed  Google Scholar 

  82. Lu Y, Miao L, Wang Y, et al. Curcumin micelles remodel tumor microenvironment and enhance vaccine activity in an advanced melanoma model. Mol Ther. 2016;24:364–74.

    Article  CAS  PubMed  Google Scholar 

  83. Khan F, Niaz K, Maqbool F, et al. Molecular targets underlying the anticancer effects of quercetin: an update. Nutrients. 2016;8:529.

    Article  PubMed Central  CAS  Google Scholar 

  84. He D, Guo X, Zhang E, et al. Quercetin induces cell apoptosis of myeloma and displays a synergistic effect with dexamethasone in vitro and in vivo xenograft models. Oncotarget. 2016;7:45489–99.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Pratheeshkumar P, Budhraja A, Son YO, et al. Quercetin inhibits angiogenesis mediated human prostate tumor growth by targeting VEGFR- 2 regulated AKT/mTOR/P70S6K signaling pathways. PLoS One. 2012;7:e47516.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Kashyap D, Mittal S, Sak K, et al. Molecular mechanisms of action of quercetin in cancer: recent advances. Tumour Biol. 2016;37:12927–39.

    Article  CAS  PubMed  Google Scholar 

  87. Sternberg Z, Chadha K, Lieberman A, et al. Quercetin and interferon-beta modulate immune response(s) in peripheral blood mononuclear cells isolated from multiple sclerosis patients. J Neuroimmunol. 2008;205:142–7.

    Article  CAS  PubMed  Google Scholar 

  88. Hamalainen M, Nieminen R, Vuorela P, et al. Anti-inflammatory effects of flavonoids: genistein, kaempferol, quercetin, and daidzein inhibit STAT-1 and NF-kappaB activations, whereas flavone, isorhamnetin, naringenin, and pelargonidin inhibit only NF-kappaB activation along with their inhibitory effect on iNOS expression and NO production in activated macrophages. Mediat Inflamm. 2007;2007:45673.

    Article  CAS  Google Scholar 

  89. Ruiz PA, Braune A, Holzlwimmer G, et al. Quercetin inhibits TNF-induced NF-kappaB transcription factor recruitment to proinflammatory gene promoters in murine intestinal epithelial cells. J Nutr. 2007;137:1208–15.

    Article  CAS  PubMed  Google Scholar 

  90. Lee J, Choi JW, Sohng JK, et al. The immunostimulating activity of quercetin 3-O-xyloside in murine macrophages via activation of the ASK1/MAPK/NF-kappaB signaling pathway. Int Immunopharmacol. 2016;31:88–97.

    Article  CAS  PubMed  Google Scholar 

  91. Spagnuolo C, Russo GL, Orhan IE, et al. Genistein and cancer: current status, challenges, and future directions. Adv Nutr. 2015;6:408–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Li F, Zhang J, Arfuso F, et al. NF-kappaB in cancer therapy. Arch Toxicol. 2015;89:711–31.

    Article  CAS  PubMed  Google Scholar 

  93. Xie J, Wang J, Zhu B. Genistein inhibits the proliferation of human multiple myeloma cells through suppression of nuclear factor-kappaB and upregulation of microRNA-29b. Mol Med Rep. 2016;13:1627–32.

    Article  CAS  PubMed  Google Scholar 

  94. Mir IA, Tiku AB. Chemopreventive and therapeutic potential of “naringenin,” a flavanone present in citrus fruits. Nutr Cancer. 2015;67:27–42.

    Article  CAS  PubMed  Google Scholar 

  95. Qin L, Jin L, Lu L, et al. Naringenin reduces lung metastasis in a breast cancer resection model. Protein Cell. 2011;2:507–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Zhang F, Dong W, Zeng W, et al. Naringenin prevents TGF-beta1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation. Breast Cancer Res. 2016;18:38.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Vanamala J, Leonardi T, Patil BS, Turner ND, et al. Suppression of colon carcinogenesis by bioactive compounds in grapefruit. Carcinogenesis. 2006;27:1257–65.

    Article  CAS  PubMed  Google Scholar 

  98. Lim W, Park S, Bazer FW, et al. Naringenin-induced apoptotic cell death in prostate cancer cells is mediated via the PI3K/AKT and MAPK signaling pathways. J Cell Biochem. 2017;118:1118–31.

    Article  CAS  PubMed  Google Scholar 

  99. Varoni EM, Lo Faro AF, Sharifi-Rad J, et al. Anticancer molecular mechanisms of resveratrol. Front Nutr. 2016;3:8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Jang M, Cai L, Udeani GO, et al. Cancer chemopreventive activity of resveratrol, a natural product derived from grapes. Science. 1997;275:218–20.

    Article  CAS  PubMed  Google Scholar 

  101. MacCarrone M, Lorenzon T, Guerrieri P, et al. Resveratrol prevents apoptosis in K562 cells by inhibiting lipoxygenase and cyclooxygenase activity. Eur J Biochem. 1999;265:27–34.

    Article  CAS  PubMed  Google Scholar 

  102. Robb EL, Stuart JA. Resveratrol interacts with estrogen receptor-beta to inhibit cell replicative growth and enhance stress resistance by upregulating mitochondrial superoxide dismutase. Free Radic Biol Med. 2011;50:821–31.

    Article  CAS  PubMed  Google Scholar 

  103. Wang J, Guo Z, Fu Y, et al. Weak-binding molecules are not drugs? Toward a systematic strategy for finding effective weak-binding drugs. Briefing Bioinf. 2017;18:321–32.

    CAS  Google Scholar 

  104. Mestres J, Gregori-Puigjane E. Conciliating binding efficiency and polypharmacology. Trends Pharmacol Sci. 2009;30:470–4.

    Article  CAS  PubMed  Google Scholar 

  105. Jeong SK, Yang K, Park YS, et al. Interferon gamma induced by resveratrol analog, HS-1793, reverses the properties of tumor associated macrophages. Int Immunopharmacol. 2014;22:303–10.

    Article  CAS  PubMed  Google Scholar 

  106. Lai X, Pei Q, Song X, et al. The enhancement of immune function and activation of NF-kappaB by resveratrol-treatment in immunosuppressive mice. Int Immunopharmacol. 2016;33:42–7.

    Article  CAS  PubMed  Google Scholar 

  107. Jiao Y, Hannafon BN, Ding WQ. Disulfiram's anticancer activity: evidence and mechanisms. Anti Cancer Agents Med Chem. 2016;16:1378–84.

    Article  CAS  Google Scholar 

  108. Chen D, Cui QC, Yang H, et al. Disulfiram, a clinically used anti-alcoholism drug and copper-binding agent, induces apoptotic cell death in breast cancer cultures and xenografts via inhibition of the proteasome activity. Cancer Res. 2006;66:10425–33.

    Article  CAS  PubMed  Google Scholar 

  109. Kim JY, Cho Y, Oh E, et al. Disulfiram targets cancer stem-like properties and the HER2/Akt signaling pathway in HER2-positive breast cancer. Cancer Lett. 2016;379:39–48.

    Article  CAS  PubMed  Google Scholar 

  110. Liu X, Wang L, Cui W, et al. Targeting ALDH1A1 by disulfiram/copper complex inhibits non-small cell lung cancer recurrence driven by ALDH-positive cancer stem cells. Oncotarget. 2016;7:58516–30.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Hatoum D, McGowan EM. Recent advances in the use of metformin: can treating diabetes prevent breast cancer? Biomed Res Int. 2015;2015:548436.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Gwak H, Kim Y, An H, et al. Metformin induces degradation of cyclin D1 via AMPK/GSK3beta axis in ovarian cancer. Mol Carcinog. 2017;56:349–58.

    Article  CAS  PubMed  Google Scholar 

  113. Gan RY, Li HB, Sui ZQ, et al. Absorption, metabolism, anticancer effect and molecular targets of epigallocatechin gallate (EGCG): an updated review. Crit Rev Food Sci Nutr. 2018;58(6):924–41.

    Article  CAS  PubMed  Google Scholar 

  114. Singh BN, Shankar S, Srivastava RK. Green tea catechin, epigallocatechin-3-gallate (EGCG): mechanisms, perspectives and clinical applications. BiochemPharmacol. 2011;82:1807–21.

    CAS  Google Scholar 

  115. Cheng CW, Shieh PC, Lin YC, et al. Indoleamine 2,3-dioxygenase, an immunomodulatory protein, is suppressed by (−)-epigallocatechin-3-gallate via blocking of gammainterferon-induced JAK-PKC-delta-STAT1 signaling in human oral cancer cells. J Agric Food Chem. 2010;58:887–94.

    Article  CAS  PubMed  Google Scholar 

  116. Shim JH, Choi HS, Pugliese A, et al. (-)-Epigallocatechin gallate regulates CD3-mediated T cell receptor signaling in leukemia through the inhibition of ZAP-70 kinase. J Biol Chem. 2008;283:28370–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Jin Y, Khadka DB, Cho WJ. Pharmacological effects of berberine and its derivatives: a patent update. Expert Opin Ther Pat. 2016;26:229–43.

    Article  CAS  PubMed  Google Scholar 

  118. Jabbarzadeh Kaboli P, Rahmat A, Ismail P, et al. Targets and mechanisms of berberine, a natural drug with potential to treat cancer with special focus on breast cancer. Eur J Pharmacol. 2014;740:584–95.

    Article  CAS  PubMed  Google Scholar 

  119. Wang N, Tan HY, Li L, et al. Berberine and Coptidis Rhizoma as potential anticancer agents: recent updates and future perspectives. J Ethnopharmacol. 2015;176:35–48.

    Article  CAS  PubMed  Google Scholar 

  120. Mantena SK, Sharma SD, Katiyar SK. Berberine, a natural product, induces G1-phase cell cycle arrest and caspase-3-dependent apoptosis in human prostate carcinoma cells. Mol Cancer Ther. 2006;5:296–308.

    Article  CAS  PubMed  Google Scholar 

  121. Ji C, Yang B, Yang YL, et al. Exogenous cell-permeable C6 ceramide sensitizes multiple cancer cell lines to Doxorubicin-induced apoptosis by promoting AMPK activation and mTORC1 inhibition. Oncogene. 2010;29:6557–68.

    Article  CAS  PubMed  Google Scholar 

  122. Lu JJ, Fu L, Tang Z, et al. Melatonin inhibits AP-2beta/hTERT, NF-kappaB/COX-2 and Akt/ERK and activates caspase/Cyto C signaling to enhance the antitumor activity of berberine in lung cancer cells. Oncotarget. 2016;7:2985–3001.

    Article  PubMed  Google Scholar 

  123. Ho YT, Yang JS, Li TC, et al. Berberine suppresses in vitro migration and invasion of human SCC-4 tongue squamous cancer cells through the inhibitions of FAK, IKK, NF-kappaB, u-PA and MMP-2 and -9. Cancer Lett. 2009;279:155–62.

    Article  CAS  PubMed  Google Scholar 

  124. Deng S, Hu B, Shen KP, et al. Inflammation, macrophage in cancer progression and Chinese herbal treatment. J Basic Clin Pharm. 2012;3:269–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Yildirim MA, Goh KI, Cusick ME, et al. Drug-target network. Nat Biotechnol. 2007;25:1119–26.

    Article  CAS  PubMed  Google Scholar 

  126. Bento AP, Gaulton A, Hersey A, et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42:D1083–90.

    Article  CAS  PubMed  Google Scholar 

  127. Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44:D1045–53.

    Article  CAS  PubMed  Google Scholar 

  128. Wang Y, Bolton E, Dracheva S, et al. An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2010;38:D255–66.

    Article  CAS  PubMed  Google Scholar 

  129. Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42:D1091–7.

    Article  CAS  PubMed  Google Scholar 

  130. Yang H, Qin C, Li YH, et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res. 2016;44:D1069–74.

    Article  CAS  PubMed  Google Scholar 

  131. Wagner AH, Coffman AC, Ainscough BJ, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016;44:D1036–44.

    Article  CAS  PubMed  Google Scholar 

  132. Kuhn M, Szklarczyk D, Pletscher-Frankild S, et al. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 2014;42:D401–7.

    Article  CAS  PubMed  Google Scholar 

  133. Nickel J, Gohlke BO, Erehman J, et al. SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 2014;42:W26–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Ye H, Ye L, Kang H, et al. HIT: linking herbal active ingredients to targets. Nucleic Acids Res. 2011;39:D1055–9.

    Article  CAS  PubMed  Google Scholar 

  135. Xue R, Fang Z, Zhang M, et al. TCMID: traditional Chinese Medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013;41:D1089–95.

    Article  CAS  PubMed  Google Scholar 

  136. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Mangal M, Sagar P, Singh H, et al. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res. 2013;41:D1124–9.

    Article  CAS  PubMed  Google Scholar 

  138. Tao W, Li B, Gao S, et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep. 2015;5:11481.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet. 2004;5:262–75.

    Article  CAS  PubMed  Google Scholar 

  140. Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35.

    Article  CAS  PubMed  Google Scholar 

  141. Duan Q, Flynn C, Niepel M, et al. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res. 2014;42:W449–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Igarashi Y, Nakatsu N, Yamashita T, et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 2015;43:D921–7.

    Article  CAS  PubMed  Google Scholar 

  143. Ganter B, Snyder RD, Halbert DN, et al. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics. 2006;7:1025–44.

    Article  CAS  PubMed  Google Scholar 

  144. Lamb J. The connectivity map: a new tool for biomedical research. Nat Rev Cancer. 2007;7:54–60.

    Article  CAS  PubMed  Google Scholar 

  145. Adams JU. Genetics: big hopes for big data. Nature. 2015;527:S108–9.

    Article  CAS  PubMed  Google Scholar 

  146. Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015;43:D470–8.

    Article  CAS  PubMed  Google Scholar 

  147. Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database–2009 update. Nucleic Acids Res. 2009;37:D767–72.

    Article  CAS  PubMed  Google Scholar 

  148. Mosca R, Ceol A, Aloy P. Interactome3D: adding structural details to protein networks. Nat Methods. 2013;10:47–53.

    Article  CAS  PubMed  Google Scholar 

  149. Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45:D362–8.

    Article  CAS  PubMed  Google Scholar 

  150. Licata L, Briganti L, Peluso D, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40:D857–61.

    Article  CAS  PubMed  Google Scholar 

  151. Cheng F, Jia P, Wang Q, et al. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget. 2014;5:3697–710.

    Article  PubMed  PubMed Central  Google Scholar 

  152. Cheng F, Liu C, Lin CC, et al. A gene gravity model for the evolution of cancer genomes: a study of 3,000 cancer genomes across 9 cancer types. PLoS Comput Biol. 2015;11:e1004497.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Zhang C, Hong H, Mendrick DL, et al. Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. BiomarkMed. 2015;9:1241–52.

    CAS  Google Scholar 

  154. Cheng F, Murray JL, Zhao J, et al. Systems biology-based investigation of cellular antiviral drug targets identified by gene-trap insertional mutagenesis. PLoS Comput Biol. 2016;12:e1005074.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  155. Cheng F, Liu C, Shen B, et al. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC Syst Biol. 2016;10(Suppl 3):65.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  156. Cheng F, Zhao J, Hanker AB, et al. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat. 2016;160:457–74.

    Article  CAS  PubMed  Google Scholar 

  157. Cheng F, Jia P, Wang Q, et al. Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol. 2014;31:2156–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Cheng F, Zhao J, Zhao Z. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform. 2016;17:642–56.

    Article  CAS  PubMed  Google Scholar 

  159. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science. 2013;339:1546–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Hayes DN, Kim WY. The next steps in next-gen sequencing of cancer genomes. J Clin Invest. 2015;125:462–8.

    Article  PubMed  PubMed Central  Google Scholar 

  161. Chin L, Andersen JN, Futreal PA. Cancer genomics: from discovery science to personalized medicine. Nat Med. 2011;17:297–303.

    Article  CAS  PubMed  Google Scholar 

  162. International Cancer Genome C, Hudson TJ, Anderson W, et al. International network of cancer genome projects. Nature. 2010;464:993–8.

    Article  CAS  Google Scholar 

  163. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  164. Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43:D805–11.

    Article  CAS  PubMed  Google Scholar 

  165. Nakagawa H, Wardell CP, Furuta M, et al. Cancer whole-genome sequencing: present and future. Oncogene. 2015;34:5943–50.

    Article  CAS  PubMed  Google Scholar 

  166. Chen X, Yan CC, Zhang X, et al. Drug-target interaction prediction: databases, web servers and computational models. Brief Bioinform. 2016;17:696–712.

    Article  CAS  PubMed  Google Scholar 

  167. Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteome. 2011;74:2554–74.

    Article  CAS  Google Scholar 

  168. Yue R, Shan L, Yang X, et al. Approaches to target profiling of natural products. Curr Med Chem. 2012;19:3841–55.

    Article  CAS  PubMed  Google Scholar 

  169. Jenkins JL, Bender A, Davies JW. In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol. 2007;3:413–21.

    Article  Google Scholar 

  170. Harren J, Andrew RL. Structure-based drug discovery. Dordrecht: Springer; 2007. ISB ISBN: 978-1-4020-4406-9

    Google Scholar 

  171. Taboureau O, Baell JB, Fernandez-Recio J, et al. Established and emerging trends in computational drug discovery in the structural genomics era. Chem Biol. 2012;19:29–41.

    Article  CAS  PubMed  Google Scholar 

  172. Sakkiah S, Ng HW, Tong W, et al. Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery. Expert Opin Ther Targets. 2016;20:1267–82.

    Article  CAS  PubMed  Google Scholar 

  173. Liu LJ, Leung KH, Chan DS, et al. Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening. Cell Death Dis. 2014;5:e1293.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Zhong HJ, Lee BR, Boyle JW, et al. Structure-based screening and optimization of cytisine derivatives as inhibitors of the menin-MLL interaction. Chem Commun. 2016;52:5788–91.

    Article  CAS  Google Scholar 

  175. Singh T, Gupta NA, Xu S, et al. Honokiol inhibits the growth of head and neck squamous cell carcinoma by targeting epidermal growth factor receptor. Oncotarget. 2015;6:21268–82.

    Article  PubMed  PubMed Central  Google Scholar 

  176. Zhong HJ, Ma VP, Cheng Z, et al. Discovery of a natural product inhibitor targeting protein neddylation by structure-based virtual screening. Biochimie. 2012;94:2457–60.

    Article  CAS  PubMed  Google Scholar 

  177. Lee HM, Chan DS, Yang F, et al. Identification of natural product fonsecin B as a stabilizing ligand of c-myc G-quadruplex DNA by high-throughput virtual screening. Chem Commun. 2010;46:4680–2.

    Article  CAS  Google Scholar 

  178. Ma D-L, Chan DS-H, Leung C-H. Molecular docking for virtual screening of natural product databases. ChemSci. 2011;2:1656–65.

    CAS  Google Scholar 

  179. Cierpicki T, Grembecka J. Challenges and opportunities in targeting the menin-MLL interaction. Future Med Chem. 2014;6:447–62.

    Article  CAS  PubMed  Google Scholar 

  180. Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43:217–26.

    Article  CAS  PubMed  Google Scholar 

  181. Wang JC, Chu PY, Chen CM, et al. idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res. 2012;40:W393–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Lauro G, Romano A, Riccio R, et al. Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds. J Nat Prod. 2011;74:1401–7.

    Article  CAS  PubMed  Google Scholar 

  183. Lauro G, Masullo M, Piacente S, et al. Inverse virtual screening allows the discovery of the biological activity of natural compounds. Bioorg Med Chem. 2012;20:3596–602.

    Article  CAS  PubMed  Google Scholar 

  184. Vuong H, Cheng F, Lin CC, et al. Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach. Genome Med. 2014;6:81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  185. Zhao J, Cheng F, Wang Y, et al. Systematic prioritization of druggable mutations in approximately 5000 genomes across 16 cancer types using a structural genomics-based approach. Mol Cell Proteomics. 2016;15:642–56.

    Article  CAS  PubMed  Google Scholar 

  186. Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20:318–31.

    Article  PubMed  Google Scholar 

  187. Yan X, Liao C, Liu Z, et al. Chemical structure similarity search for ligand-based virtual screening: methods and computational resources. Curr Drug Targets. 2016;17:1580–5.

    Article  CAS  PubMed  Google Scholar 

  188. Liu X, Ouyang S, Yu B, et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 2010;38:W609–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Yan X, Li J, Liu Z, et al. Enhancing molecular shape comparison by weighted Gaussian functions. J Chem Inf Model. 2013;53:1967–78.

    Article  CAS  PubMed  Google Scholar 

  190. Fang J, Yang R, Gao L, et al. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model. 2013;53:3009–20.

    Article  CAS  PubMed  Google Scholar 

  191. Fang J, Yang R, Gao L, et al. Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery. Mol Divers. 2015;19:149–62.

    Article  CAS  PubMed  Google Scholar 

  192. Fang J, Li Y, Liu R, et al. Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J Chem Inf Model. 2015;55:149–64.

    Article  CAS  PubMed  Google Scholar 

  193. Fang J, Pang X, Wu P, et al. Discovery of neuroprotective compounds by machine learning approaches. RSC Adv. 2016;6:9857.

    Article  CAS  Google Scholar 

  194. Cheng F, Li W, Liu G, et al. In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem. 2013;13:1273–89.

    Article  CAS  PubMed  Google Scholar 

  195. Sprague B, Shi Q, Kim MT, et al. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. J Comput Aided Mol Des. 2014;28:631–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Bilsland AE, Pugliese A, Liu Y, et al. Identification of a selective G1-phase benzimidazolone inhibitor by a senescence targeted virtual screen using artificial neural networks. Neoplasia. 2015;17:704–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Liu H, Sun J, Guan J, et al. Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics. 2015;31:i221–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  199. Yamanishi Y. Chemogenomic approaches to infer drug target interaction networks. Methods Mol Biol. 2013;939:97–113.

    Article  CAS  PubMed  Google Scholar 

  200. Zhao S, Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One. 2010;5:e11764.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  201. Yamanishi Y, Araki M, Gutteridge A, et al. Prediction of drug target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24:i232–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Bleakley K, Yamanishi Y. Supervised prediction of drug target interactions using bipartite local models. Bioinformatics. 2009;25:2397–403.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Keum J, Yoo S, Lee D, et al. Prediction of compound-target interactions of natural products using large-scale drug and protein information. BMC Bioinformatics. 2016;17(Suppl 6):219.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  204. Yu H, Chen J, Xu X, et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One. 2012;7:e37608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Huang C, Zheng C, Li Y, et al. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief Bioinform. 2014;15:710–33.

    Article  PubMed  Google Scholar 

  206. Sawada R, Kotera M, Yamanishi Y. Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach. Mol Inform. 2014;33:719–31.

    Article  CAS  PubMed  Google Scholar 

  207. Cheng F, Zhou Y, Li J, et al. Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol BioSyst. 2012;8:2373–84.

    Article  CAS  PubMed  Google Scholar 

  208. Cheng F, Li W, Zhou Y, et al. Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic derived models (PTDMs). Mol BioSyst. 2013;9:1316–25.

    Article  CAS  PubMed  Google Scholar 

  209. Kitano H. Systems biology: a brief overview. Science. 2002;295:1662–4.

    Article  CAS  PubMed  Google Scholar 

  210. Kitano H. Computational systems biology. Nature. 2002;420:206–10.

    Article  CAS  PubMed  Google Scholar 

  211. Berg EL. Systems biology in drug discovery and development. Drug Discov Today. 2014;19:113–25.

    Article  CAS  PubMed  Google Scholar 

  212. Cheng F, Murray JL, Rubin DH. Drug repurposing: new treatments for Zika virus infection? Trends Mol Med. 2016;22:919–21.

    Article  PubMed  Google Scholar 

  213. Qu XA, Rajpal DK. Applications of connectivity map in drug discovery and development. Drug Discov Today. 2012;17:1289–98.

    Article  CAS  PubMed  Google Scholar 

  214. Hieronymus H, Lamb J, Ross KN, et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell. 2006;10:321–30.

    Article  CAS  PubMed  Google Scholar 

  215. Wei G, Twomey D, Lamb J, et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell. 2006;10:331–42.

    Article  CAS  PubMed  Google Scholar 

  216. Langley SR, Dwyer J, Drozdov I, et al. Proteomics: from single molecules to biological pathways. Cardiovasc Res. 2013;97:612–22.

    Article  CAS  PubMed  Google Scholar 

  217. Bensimon A, Heck AJ, Aebersold R. Mass spectrometry-based proteomics and network biology. Annu Rev Biochem. 2012;81:379–405.

    Article  CAS  PubMed  Google Scholar 

  218. Savitski MM, Reinhard FB, Franken H, et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science. 2014;346:1255784.

    Article  PubMed  CAS  Google Scholar 

  219. Franken H, Mathieson T, Childs D, et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat Protoc. 2015;10:1567–93.

    Article  CAS  PubMed  Google Scholar 

  220. Reinhard FB, Eberhard D. Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat Methods. 2015;12:1129–31.

    Article  CAS  PubMed  Google Scholar 

  221. Sacco F, Silvestri A, Posca D, et al. Deep proteomics of breast cancer cells reveals that metformin rewires signaling networks away from a pro-growth state. Cell Syst. 2016;2:159–71.

    Article  CAS  PubMed  Google Scholar 

  222. Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. Metabolomics: a global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol. 2008;48:653–83.

    Article  CAS  PubMed  Google Scholar 

  223. Birkenstock T, Liebeke M, Winstel V, et al. Exometabolome analysis identifies pyruvate dehydrogenase as a target for the antibiotic triphenylbismuthdichloride in multiresistant bacterial pathogens. J Biol Chem. 2012;287:2887–95.

    Article  CAS  PubMed  Google Scholar 

  224. Zhang B, Watts KM, Hodge D, et al. A second target of the antimalarial and antibacterial agent fosmidomycin revealed by cellular metabolic profiling. Biochemistry. 2011;50:3570–7.

    Article  CAS  PubMed  Google Scholar 

  225. Bayet-Robert M, Lim S, Barthomeuf C, et al. Biochemical disorders induced by cytotoxic marine natural products in breast cancer cells as revealed by proton NMR spectroscopy-based metabolomics. Biochem Pharmacol. 2010;80:1170–9.

    Article  CAS  PubMed  Google Scholar 

  226. Pulido MR, Garcia-Quintanilla M, Gil-Marques ML, et al. Identifying targets for antibiotic development using omics technologies. Drug Discov Today. 2016;21:465–72.

    Article  CAS  PubMed  Google Scholar 

  227. Zhao Y, Hu Q, Cheng F, et al. SoNar, a highly responsive NADþ/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metab. 2015;21:777–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Kim HK, Wilson EG, Choi YH, et al. Metabolomics: a tool for anticancer lead-finding from natural products. Planta Med. 2010;76:1094–102.

    Article  CAS  PubMed  Google Scholar 

  229. Collins GS, de Groot JA, Dutton S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, Z., Yang, B. (2022). Polypharmacology and Natural Products. In: Polypharmacology. Springer, Cham. https://doi.org/10.1007/978-3-031-04998-9_15

Download citation

Publish with us

Policies and ethics