Abstract
Since antiquity, natural resources, mainly plants, have been used for medicinal purposes. The primitive usage was based on a trial-and-error strategy. When time passed, humans started to look deeper into the actual elements responsible for the cure. They started using plant extracts as medicines. In 1806, there opened a new window in the area of natural product-based medicines when Friedrich Sertürner, a German pharmacist, isolated morphine from the poppy plants. This is just the beginning of a new era. Soon, the extraction of phytochemicals i.e., plant-based chemical compounds, became common and chemists started looking for new ways of manipulating them and synthesizing them in laboratories. Charles Frédéric Gerhardt first synthesized acetylsalicylic acid, the wonder drug Aspirin by treating acetyl chloride and sodium salicylate in 1853. This was a revolution in drug discovery which is still running towards more advancements and developments. Later on, computers came into the picture, and chemists and molecular biologists started to visualize and analyze chemical compounds. The emergence of advancements in computers set the foundation stone for a new field of science called bioinformatics. Soon began the applications of computers in medicinal research and a new trend of computer-aided drug discovery started. Which broadly changed the face of the natural product (NP) based drug discovery process. We have huge libraries of NP-based compounds utilized to discover new drugs against several life-threatening diseases, including cancer. This chapter talks about applications of computational methods mainly belonging to Bioinformatics and Chemoinformatics that are applied towards NP-based drug discovery.
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References
Atanasov AG, Waltenberger B, Pferschy-Wenzig EM, Linder T, Wawrosch C, Uhrin P et al (2015) Discovery and resupply of pharmacologically active plant-derived natural products: a review. Biotechnol Adv 33(8):1582–1614. https://doi.org/10.1016/j.biotechadv.2015.08.001
Bernardini S, Tiezzi A, Laghezza Masci V, Ovidi E (2018) Natural products for human health: an historical overview of the drug discovery approaches. Nat Prod Res 32(16):1926–1950. https://doi.org/10.1080/14786419.2017.1356838
Chen Y, Bilban M, Foster CA, Boger DL (2002) Solution-phase parallel synthesis of a pharmacophore library of HUN-7293 analogues: a general chemical mutagenesis approach to defining structure-function properties of naturally occurring cyclic (depsi)peptides. J Am Chem Soc 124(19):5431–5440. https://doi.org/10.1021/ja020166v
Chen Y, de Bruyn Kops C, Kirchmair J (2017) Data resources for the computer-guided discovery of bioactive natural products. J Chem Inf Model 57(9):2099–2111. https://doi.org/10.1021/acs.jcim.7b00341
Cozza G, Bonvini P, Zorzi E, Poletto G, Pagano MA, Sarno S et al (2006) Identification of ellagic acid as potent inhibitor of protein kinase CK2: a successful example of a virtual screening application. J Med Chem 49(8):2363–2366. https://doi.org/10.1021/jm060112m
Cragg GM, Newman DJ, Snader KM (1997) Natural products in drug discovery and development. J Nat Prod 60(1):52–60. https://doi.org/10.1021/np9604893
Dhiman P, Malik N, Khatkar A (2018) 3D-QSAR and in-silico studies of natural products and related derivatives as monoamine oxidase inhibitors. Curr Neuropharmacol 16(6):881–900. https://doi.org/10.2174/1570159X15666171128143650
Dias DA, Urban S, Roessner U (2012) A historical overview of natural products in drug discovery. Meta 2(2):303–336. https://doi.org/10.3390/metabo2020303
Grabowski K, Baringhaus K-H, Schneider G (2008) Scaffold diversity of natural products: inspiration for combinatorial library design. Nat Prod Rep 25(5):892–904. https://doi.org/10.1039/B715668P
Harvey AL, Edrada-Ebel R, Quinn RJ (2015) The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 14(2):111–129. https://doi.org/10.1038/nrd4510
Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ et al (2015) Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 216:574–578
Khan MT, Orhan I, Senol FS, Kartal M, Sener B, Dvorska M et al (2009) Cholinesterase inhibitory activities of some flavonoid derivatives and chosen xanthone and their molecular docking studies. Chem Biol Interact 181(3):383–389. https://doi.org/10.1016/j.cbi.2009.06.024
Korotcov A, Tkachenko V, Russo DP, Ekins S (2017) Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 14(12):4462–4475. https://doi.org/10.1021/acs.molpharmaceut.7b00578
Lagunin AA, Goel RK, Gawande DY, Pahwa P, Gloriozova TA, Dmitriev AV et al (2014) Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Nat Prod Rep 31(11):1585–1611. https://doi.org/10.1039/c4np00068d
Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20(3):318–331. https://doi.org/10.1016/j.drudis.2014.10.012
Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53(2):539–558. https://doi.org/10.1021/jm900817u
Liu R, Li X, Lam KS (2017) Combinatorial chemistry in drug discovery. Curr Opin Chem Biol 38:117–126. https://doi.org/10.1016/j.cbpa.2017.03.017
Liu M, Karuso P, Feng Y, Kellenberger E, Liu F, Wang C, Quinn RJ (2019) Is it time for artificial intelligence to predict the function of natural products based on 2D-structure. Medchemcomm 10(10):1667–1677. https://doi.org/10.1039/c9md00128j
Lusher SJ, McGuire R, van Schaik RC, Nicholson CD, de Vlieg J (2014) Data-driven medicinal chemistry in the era of big data. Drug Discov Today 19(7):859–868. https://doi.org/10.1016/j.drudis.2013.12.004
Ma D-L, Chan DS-H, Leung C-H (2011) Molecular docking for virtual screening of natural product databases. Chem Sci 2(9):1656–1665. https://doi.org/10.1039/C1SC00152C
Mang C, Jakupovic S, Schunk S, Ambrosi H-D, Schwarz O, Jakupovic J (2006) Natural products in combinatorial chemistry: an andrographolide-based library. J Comb Chem 8(2):268–274. https://doi.org/10.1021/cc050143n
McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB et al (2011) The eMERGE network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genet 4:13. https://doi.org/10.1186/1755-8794-4-13
Mladenovic M, Patsilinakos A, Pirolli A, Sabatino M, Ragno R (2017) Understanding the molecular determinant of reversible human monoamine oxidase B inhibitors containing 2H-Chromen-2-one core: structure-based and ligand-based derived three-dimensional quantitative structure-activity relationships predictive models. J Chem Inf Model 57(4):787–814. https://doi.org/10.1021/acs.jcim.6b00608
Naqvi AAT, Hassan MI (2017) Methods for docking and drug designing. In: Oncology: breakthroughs in research and practice. IGI Global, Philadelphia, pp 876–890
Naqvi AAT, Mohammad T, Hasan GM, Hassan MI (2018) Advancements in docking and molecular dynamics simulations towards ligand–receptor interactions and structure-function relationships. Curr Top Med Chem 18(20):1755–1768. https://doi.org/10.2174/1568026618666181025114157
Nasution MAF, Toepak EP, Alkaff AH, Tambunan USF (2018) Flexible docking-based molecular dynamics simulation of natural product compounds and Ebola virus Nucleocapsid (EBOV NP): a computational approach to discover new drug for combating Ebola. BMC Bioinform 19(suppl 14):419. https://doi.org/10.1186/s12859-018-2387-8
Newman DJ, Cragg GM (2016) Natural products as sources of new drugs from 1981 to 2014. J Nat Prod 79(3):629–661. https://doi.org/10.1021/acs.jnatprod.5b01055
Nguyen-Vo TH, Nguyen L, Do N, Nguyen TN, Trinh K, Cao H, Le L (2020) Plant metabolite databases: from herbal medicines to modern drug discovery. J Chem Inf Model 60(3):1101–1110. https://doi.org/10.1021/acs.jcim.9b00826
Norn S, Permin H, Kruse PR, Kruse E (2009) From willow bark to acetylsalicylic acid. Dan Medicinhist Arbog 37:79–98
Onguene PA, Simoben CV, Fotso GW, Andrae-Marobela K, Khalid SA, Ngadjui BT et al (2018) In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties. Comput Biol Chem 72:136–149. https://doi.org/10.1016/j.compbiolchem.2017.12.002
Pathan H, Williams J (2012) Basic opioid pharmacology: an update. Br J Pain 6(1):11–16. https://doi.org/10.1177/2049463712438493
Patridge E, Gareiss P, Kinch MS, Hoyer D (2016) An analysis of FDA-approved drugs: natural products and their derivatives. Drug Discov Today 21(2):204–207. https://doi.org/10.1016/j.drudis.2015.01.009
Perez A, Martinez-Rosell G, De Fabritiis G (2018) Simulations meet machine learning in structural biology. Curr Opin Struct Biol 49:139–144. https://doi.org/10.1016/j.sbi.2018.02.004
Petrovska BB (2012) Historical review of medicinal plants’ usage. Pharmacogn Rev 6(11):1–5. https://doi.org/10.4103/0973-7847.95849
Romano JD, Tatonetti NP (2019) Informatics and computational methods in natural product drug discovery: a review and perspectives. Front Genet 10:368. https://doi.org/10.3389/fgene.2019.00368
Shi C, Dong F, Zhao G, Zhu N, Lao X, Zheng H (2020) Applications of machine-learning methods for the discovery of NDM-1 inhibitors. Chem Biol Drug Des 96:1232. https://doi.org/10.1111/cbdd.13708
Tatonetti NP, Ye PP, Daneshjou R, Altman RB (2012) Data-driven prediction of drug effects and interactions. Sci Transl Med 4(125):125ra131. https://doi.org/10.1126/scitranslmed.3003377
Thomford NE, Senthebane DA, Rowe A, Munro D, Seele P, Maroyi A, Dzobo K (2018) Natural products for drug discovery in the 21st century: innovations for novel drug discovery. Int J Mol Sci 19(6):1578. https://doi.org/10.3390/ijms19061578
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18(6):463–477. https://doi.org/10.1038/s41573-019-0024-5
Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10(1):95–115. https://doi.org/10.2174/156802610790232260
Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11–12):444–450. https://doi.org/10.1016/j.drudis.2010.03.013
Yao L, Zhang Y, Li Y, Sanseau P, Agarwal P (2011) Electronic health records: implications for drug discovery. Drug Discov Today 16(13–14):594–599. https://doi.org/10.1016/j.drudis.2011.05.009
Zhang DW, Luo RH, Xu L, Yang LM, Xu XS, Zheng YT, Luo H (2020) Natural-product-library-based screening for discovery of capsid C-terminal domain targeted HIV-1 inhibitors. Int J Antimicrob Agents 55(4):105926. https://doi.org/10.1016/j.ijantimicag.2020.105926
Zhu F, Li XX, Yang SY, Chen YZ (2018) Clinical success of drug targets prospectively predicted by in silico study. Trends Pharmacol Sci 39(3):229–231. https://doi.org/10.1016/j.tips.2017.12.002
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Naqvi, A.A.T., Rizvi, S.A.M., Hassan, M.I. (2023). Applications of Computational Methods in Natural Products Based Drug Discovery. In: Singh, A., Rathi, B., Verma, A.K., Singh, I.K. (eds) Natural Product Based Drug Discovery Against Human Parasites. Springer, Singapore. https://doi.org/10.1007/978-981-19-9605-4_2
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