Abstract
The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- BRD:
-
Bromodomain
- CDPs:
-
Consensus Diversity Plots
- DNMT:
-
DNA methyltransferase
- FDA:
-
Food and Drug Administration
- HDAC:
-
Histone deacetylase
- hERG:
-
Human ether-a-go-go-related gene ion-channel
- IMPS:
-
Invalid metabolic panaceas
- MACCS:
-
Molecular Access System
- PAINS:
-
Pan-Assay Interference compounds
- PCA:
-
Principal component analysis
- SAH:
-
S-adenosyl homocysteine
- SAM:
-
S-adenosyl methionine
- SMILES:
-
Simplified Molecular Input Line Entries
- TCM:
-
Traditional Chinese Medicine
- UNPD:
-
Universal Natural Products Database
References
Perry NSL, Bollen C, Perry EK, Ballard C (2003) Salvia for dementia therapy: review of pharmacological activity and pilot tolerability clinical trial. Pharmacol Biochem Behav 75:651
Astudillo-Vázquez A, Dávalos Valle H, De Jesús L, Herrera G, Navarrete A (2008) Investigation of Alternanthera repens and Bidens odorata on gastrointestinal disease. Fitoterapia 79:577
Baum SS, Hill R, Rommelspacher H (1998) Effect of kava extract and individual kavapyrones on neurotransmitter levels in the nucleus accumbens of rats. Prog Neuro-Psychopharmacol Biol Psychiatry 22:1105
Chavkin C (2003) Salvinorin A, an active component of the hallucinogenic sage Salvia divinorum is a highly efficacious opioid receptor agonist: structural and functional considerations. J Pharmacol Exp Ther 308:1197
Öztürk Y, Aydin S, Beis R, Başer KH, Berberoĝlu H (1996) Effects of Hypericum perforatum L. and Hypericum calycinum L. extracts on the central nervous system in mice. Phytomedicine 3:139
Dias DA, Urban S, Roessner U (2012) A historical overview of natural products in drug discovery. Metabolites 2:303
Beutler JA (2009) Natural products as a foundation for drug discovery. Curr Protoc Pharmacol 46:9
Harvey AL (2008) Natural products in drug discovery. Drug Discov Today 13:894
Ortholand JY, Ganesan A (2004) Natural products and combinatorial chemistry: back to the future. Curr Opin Chem Biol 8:271
Macarron R, Banks MN, Bojanic D, Burns DJ, Cirovic DA, Garyantes T, Green DV, Hertzberg RP, Janzen WP, Paslay JW, Schopfer U, Sittampalam GS (2011) Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10:188
Ganesan A (2004) Natural products as a hunting ground for combinatorial chemistry. Curr Opin Biotechnol 15:584
Cragg GM, Newman DJ (2013) Natural products: a continuing source of novel drug leads. Biochim Biophys Acta, Gen Subj 1830:3670
Pereira F, Aires-de-Sousa J (2018) Computational methodologies in the exploration of marine natural product leads. Mar Drugs 16:236
Saldívar-González FI, Pilón-Jiménez BA, Medina-Franco JL (2018) Chemical space of naturally occurring compounds. Phys Sci Rev. https://doi.org/10.1515/psr-2018-0103
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 19:1578
González-Medina M, Naveja JJ, Sánchez-Cruz N, Medina-Franco JL (2017) Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Adv 7:54153
Baell JB, Nissink JWM (2018) Seven year itch: pan-assay interference compounds (PAINS) in 2017 – utility and limitations. ACS Chem Biol 13:36
Willett P (2011) Chemoinformatics: a history. Wiley Interdiscip Rev Comput Mol Sci 1:46
Engel T (2006) Basic overview of chemoinformatics. J Chem Inf Model 46:2267
Opassi G, Gesù A, Massarotti A (2018) The hitchhiker’s guide to the chemical-biological galaxy. Drug Discov Today 23:565
Maggiora GM, Shanmugasundaram V (2011) Molecular similarity measures. Humana, Totowa, NJ, p 39
Lill MA (2007) Multi-dimensional QSAR in drug discovery. Drug Discov Today 12:1013
Prieto-Martínez FD, Medina-Franco JL (2018) Molecular docking: current advances and challenges. TIP Rev Espec Ciencias Químico-Biológicas 25:65
Schlick T (2010) Molecular dynamics: basics. In: Molecular modeling and simulation. An interdisciplinary guide, 2nd edn. Springer, New York, p 425
Parenti MD, Rastelli G (2012) Advances and applications of binding affinity prediction methods in drug discovery. Biotechnol Adv 30:244
Lavecchia A, Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839
Rollinger JM, Stuppner H, Langer T (2008) Virtual screening for the discovery of bioactive natural products. Prog Drug Res 65:211
Ma D-L, Chan DS-H, Leung C-H (2011) Molecular docking for virtual screening of natural product databases. Chem Sci 2:1656
Kubinyi H (2008) QSAR: Hansch analysis and related approaches. VCH, Weinheim
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64:4
Leeson PD (2015) Molecular inflation, attrition and the rule of five. Adv Drug Deliv Rev 101:22
Deshpande M, Kuramochi M, Karypis G (2007) Data mining algorithms for virtual screening of bioactive compounds. Springer Optim Appl 7:59
Rathi PC, Ludlow RF, Hall RJ, Murray CW, Mortenson PN, Verdonk ML (2017) Predicting “hot” and “warm” spots for fragment binding. J Med Chem 60:4036
Cerqueira NMFSA, Gesto D, Oliveira EF, Santos-Martins D, Brás NF, Sousa SF, Fernandes PA, Ramos MJ (2015) Receptor-based virtual screening protocol for drug discovery. Arch Biochem Biophys 582:56
Wingert BM, Camacho CJ (2018) Improving small molecule virtual screening strategies for the next generation of therapeutics. Curr Opin Chem Biol 44:87
McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11:494
Spyrakis F, Cavasotto CN (2015) Open challenges in structure-based virtual screening: receptor modeling, target flexibility consideration and active site water molecules description. Arch Biochem Biophys 583:105
Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7:20
Tanrikulu Y, Krüger B, Proschak E (2013) The holistic integration of virtual screening in drug discovery. Drug Discov Today 18:358
Cruz-Monteagudo M, Medina-Franco JL, Pérez-Castillo Y, Nicolotti O, Cordeiro MN, Borges F (2014) Activity cliffs in drug discovery: Dr. Jekyll or Mr. Hyde? Drug Discov Today 19:1069
Kukol A (2011) Consensus virtual screening approaches to predict protein ligands. Eur J Med Chem 46:4661
Kirchweger B, Rollinger JM (2018) Virtual screening for the discovery of active principles from natural products. In: Natural products as source of molecules with therapeutic potential, p 333
Scotti L, Bezerra Mendonca FJ, Ribeiro FF, Tavares JF, da Silva MS, Barbosa Filho JM, Scotti MT (2018) Natural product inhibitors of topoisomerases: review and docking study. Curr Protein Pept Sci 19:275
Jenkins JL, Bender A, Davies JW (2006) In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol 3:413
Szyf M (2015) Epigenetics, a key for unlocking complex CNS disorders? Therapeutic implications. Eur Neuropsychopharmacol 25:682
Biswas S, Rao CM (2018) Epigenetic tools (the writers, the readers and the erasers) and their implications in cancer therapy. Eur J Pharmacol 837:8
Schwenk RW, Vogel H, Schürmann A (2013) Genetic and epigenetic control of metabolic health. Mol Metab 2:337
Paneni F, Costantino S, Volpe M, Lüscher TF, Cosentino F (2013) Epigenetic signatures and vascular risk in type 2 diabetes: a clinical perspective. Atherosclerosis 230:191
Wilting RH, Dannenberg J-H (2012) Epigenetic mechanisms in tumorigenesis, tumor cell heterogeneity and drug resistance. Drug Resist Updat 15:21
Miousse IR, Currie R, Datta K, Ellinger-Ziegelbauer H, French JE, Harrill AH, Koturbash I, Lawton M, Mann D, Meehan RR, Moggs JG, O'Lone R, Rasoulpour RJ, Pera RA, Thompson K (2015) Importance of investigating epigenetic alterations for industry and regulators: an appraisal of current efforts by the Health and Environmental Sciences Institute. Toxicology 335:11
Wegner M, Neddermann D, Piorunska-Stolzmann M, Jagodzinski PP (2014) Role of epigenetic mechanisms in the development of chronic complications of diabetes. Diabetes Res Clin Pract 105:164
Cabaye A, Nguyen KT, Liu L, Pande V, Schapira M (2015) Structural diversity of the epigenetics pocketome. Proteins Struct Funct Bioinf 83:1316
Pande V (2016) Understanding the complexity of epigenetic target space. J Med Chem 59:1299
Priestley CC, Anderton M, Doherty AT, Duffy P, Mellor HR, Powella H, Robertsa R (2012) Epigenetics – relevance to drug safety science. Toxicol Res 1:23
Shortt J, Ott CJ, Johnstone RW, Bradner JE (2017) A chemical probe toolbox for dissecting the cancer epigenome. Nat Rev Cancer 17:160
Fischle W, Schwarzer D (2016) Probing chromatin-modifying enzymes with chemical tools. ACS Chem Biol 11:689
Singh M, Kaur M, Silakari O (2014) Flavones: an important scaffold for medicinal chemistry. Eur J Med Chem 84:206
Vasantha Rupasinghe HP, Nair SVG, Robinson RA (2014) Chemopreventive properties of fruit phenolic compounds and their possible mode of actions, 1st edn. Elsevier, Amsterdam
Ferguson FM, Fedorov O, Chaikuad A, Philpott M, Muniz JR, Felletar I, von Delft F, Heightman T, Knapp S, Abell C, Ciulli A (2013) Targeting low-druggability bromodomains: fragment based screening and inhibitor design against the BAZ2B bromodomain. J Med Chem 56:10183
Prinjha RK, Witherington J, Lee K (2012) Place your BETs: the therapeutic potential of bromodomains. Trends Pharmacol Sci 33:146
Prieto-Martínez FD, Fernandez-de Gortari E, Méndez-Lucio O, Medina-Franco JL (2016) A chemical space odyssey of inhibitors of histone deacetylases and bromodomains. RSC Adv 6:56225
Zhao H, Gartenmann L, Dong J, Spiliotopoulos D, Caflisch A (2014) Discovery of BRD4 bromodomain inhibitors by fragment-based high-throughput docking. Bioorg Med Chem Lett 24:2493
Hoffer L, Voitovich YV, Raux B, Carrasco K, Muller C, Fedorov AY, Derviaux C, Amouric A, Betzi S, Horvath D, Varnek A, Collette Y, Combes S, Roche P, Morelli X (2018) Integrated strategy for lead optimization based on fragment growing: the diversity-oriented-target-focused-synthesis approach. J Med Chem 61:5719
Tanaka M, Roberts JM, Seo H-S, Souza A, Paulk J, Scott TG, DeAngelo SL, Dhe-Paganon S, Bradner JE (2016) Design and characterization of bivalent BET inhibitors. Nat Chem Biol 12:1089
Spiliotopoulos D, Caflisch A (2014) Molecular dynamics simulations of bromodomains reveal binding-site flexibility and multiple binding modes of the natural ligand acetyl-lysine. Isr J Chem 54:1084
Prieto-Martínez FD, Medina-Franco JL (2018) Charting the bromodomain BRD4: towards the identification of novel inhibitors with molecular similarity and receptor mapping. Lett Drug Des Discov 15:1
Tarallo V, Lepore L, Marcellini M, Dal Piaz F, Tudisco L, Ponticelli S, Lund FW, Roepstorff P, Orlandi A, Pisano C, De Tommasi N, De Falco S (2011) The biflavonoid amentoflavone inhibits neovascularization preventing the activity of proangiogenic vascular endothelial growth factors. J Biol Chem 286:19641
Liu H, Yue Q, He S (2017) Amentoflavone suppresses tumor growth in ovarian cancer by modulating Skp2. Life Sci 189:96
Dhananjayan K (2015) Molecular docking study characterization of rare flavonoids at the Nac-binding site of the first bromodomain of BRD4 (BRD4 BD1). J Cancer Res 2015:1
Raj U, Kumar H, Varadwaj PK (2016) Molecular docking and dynamics simulation study of flavonoids as BET bromodomain inhibitors. J Biomol Struct Dyn 1102:1
Waterman MJ, Nugraha AS, Hendra R, Ball GE, Robinson SA, Keller PA (2017) Antarctic moss biflavonoids show high antioxidant and ultraviolet-screening activity. J Nat Prod 80:2224
Bharatham N, Slavish PJ, Young BM, Shelat AA (2018) The role of ZA channel water-mediated interactions in the design of bromodomain-selective BET inhibitors. J Mol Graph Model 81:197
Jung M, Philpott M, Müller S, Schulze J, Badock V, Eberspächer U, Moosmayer D, Bader B, Schmees N, Fernández-Montalván A, Haendler B (2014) Affinity map of bromodomain protein 4 (BRD4) interactions with the histone H4 tail and the small molecule inhibitor JQ1. J Biol Chem 289:9304
Kharenko OA, Gesner EM, Patel RG, Norek K, White A, Fontano E, Suto RK, Young PR, McLure KG, Hansen HC (2016) RVX-297 — a novel BD2 selective inhibitor of BET bromodomains. Biochem Biophys Res Commun 477:62
Prieto-Martínez FD, Medina-Franco JL (2018) Flavonoids as putative epi-modulators: insight into their binding mode with BRD4 bromodomains using molecular docking and dynamics. Biomolecules 8:61
Shadrick WR, Slavish PJ, Chai SC, Waddell B, Connelly M, Low JA, Tallant C, Young BM, Bharatham N, Knapp S, Boyd VA, Morfouace M, Roussel MF, Chen T, Lee RE, Kiplin Guy R, Shelat AA, Potter PM (2018) Exploiting a water network to achieve enthalpy-driven, bromodomain-selective BET inhibitors. Bioorg Med Chem 26:25
Guha M (2015) HDAC inhibitors still need a home run, despite recent approval. Nat Rev Drug Discov 14:225
Robert C, Rassool FV (2012) HDAC inhibitors. In: Histone deacetylase inhibitors as cancer therapeutics, 1st edn. Elsevier, Amsterdam, p 87
Zhu S, Dong Z, Ke X, Hou J, Zhao E, Zhang K, Wang F, Yang L, Xiang Z, Cui H (2018) The roles of sirtuins family in cell metabolism during tumor development. Semin Cancer Biol. https://doi.org/10.1016/j.semcancer.2018.11.003
Jing H, Lin H (2015) Sirtuins in epigenetic regulation. Chem Rev 115:2350
Wątroba M, Dudek I, Skoda M, Stangret A, Rzodkiewicz P, Szukiewicz D (2017) Sirtuins, epigenetics and longevity. Ageing Res Rev 40:11
Dai H, Sinclair DA, Ellis JL, Steegborn C (2018) Sirtuin activators and inhibitors: promises, achievements, and challenges. Pharmacol Ther 188:140
Ueda H, Nakajima H, Hori Y, Fujita T, Nishimura M, Goto T, Okuhara M (1994) FR901228, a novel antitumor bicyclic depsipeptide produced by Chromobacterium violaceum No. 968. II. Structure determination. J Antibiot 47:301
Robey RW, Chakraborty AR, Basseville A, Luchenko V, Bahr J, Zhan Z, Bates SE (2011) Histone deacetylase inhibitors: emerging mechanisms of resistance. Mol Pharmaceutics 8:2021
Konstantinopoulos PA, Vandoros GP, Papavassiliou AG (2006) FK228 (depsipeptide): a HDAC inhibitor with pleiotropic antitumor activities. Cancer Chemother Pharmacol 58:711
VanderMolen KM, McCulloch W, Pearce CJ, Oberlies NH (2011) Romidepsin (Istodax, NSC 630176, FR901228, FK228, depsipeptide): a natural product recently approved for cutaneous T-cell lymphoma. J Antibiot 64:525
Cherblanc FL, Davidson RWM, Di Fruscia P, Srimongkolpithak N, Fuchter MJ (2013) Perspectives on natural product epigenetic modulators in chemical biology and medicine. Nat Prod Rep 30:605
Neugebauer RC, Uchiechowska U, Meier R, Hruby H, Valkov V, Verdin E, Sippl W, Jung M (2008) Structure-activity studies on splitomicin derivatives as sirtuin inhibitors and computational prediction of binding mode. J Med Chem 51:1203
Kokkonen P, Mellini P, Nyrhilä O, Rahnasto-Rilla M, Suuronen T, Kiviranta P, Huhtiniemi T, Poso A, Jarho E, Lahtela-Kakkonen M (2014) Quantitative insights for the design of substrate-based SIRT1 inhibitors. Eur J Pharm Sci 59:12
Sun Y, Zhou H, Zhu H, Leung SW (2016) Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products. Sci Rep 6:1
Wang Y, Liang X, Chen Y, Zhao X (2016) Screening SIRT1 activators from medicinal plants as bioactive compounds against oxidative damage in mitochondrial function. Oxidative Med Cell Longev 2016:1
Karaman B, Alhalabi Z, Swyter S, Mihigo SO, Andrae-Marobela K, Jung M, Sippl W, Ntie-Kang F (2018) Identification of bichalcones as sirtuin inhibitors by virtual screening and in vitro testing. Molecules 23:1
Wang Y, He J, Liao M, Hu M, Li W, Ouyang H, Wang X, Ye T, Zhang Y, Ouyang L (2019) An overview of sirtuins as potential therapeutic target: structure, function and modulators. Eur J Med Chem 161:48
Rahnasto-Rilla M, Tyni J, Huovinen M, Jarho E, Kulikowicz T, Ravichandran S, A Bohr V, Ferrucci L, Lahtela-Kakkonen M, Moaddel R (2018) Natural polyphenols as sirtuin 6 modulators. Sci Rep 8:1
Religa AA, Waters AP (2012) Sirtuins of parasitic protozoa: in search of function(s). Mol Biochem Parasitol 185:71
Mittal N, Muthuswami R, Madhubala R (2017) The mitochondrial SIR2 related protein 2 (SIR2RP2) impacts Leishmania donovani growth and infectivity. PLoS Negl Trop Dis 1:e0005590
Ritagliati C, Alonso VL, Manarin R, Cribb P, Serra EC (2015) Overexpression of cytoplasmic TcSIR2RP1 and mitochondrial TcSIR2RP3 impacts on Trypanosoma cruzi growth and cell invasion. PLoS Negl Trop Dis 9:1
Kadam RU, Tavares J, Kiran VM, Cordeiro A, Ouaissi A, Roy N (2008) Structure function analysis of Leishmania sirtuin: an ensemble of in silico and biochemical studies. Chem Biol Drug Des 71:501
Soares MBP, Silva CV, Bastos TM, Guimarães ET, Figueira CP, Smirlis D, Azevedo WF Jr (2012) Anti-Trypanosoma cruzi activity of nicotinamide. Acta Trop 122:224
Rose NR, Klose RJ (2014) Understanding the relationship between DNA methylation and histone lysine methylation. Biochim Biophys Acta — Gene Regul Mech 1839:1362
Liu Y, Liu K, Qin S, Xu C, Min J (2014) Epigenetic targets and drug discovery: Part 1: histone methylation. Pharmacol Ther 143:275
Zhang J, Zheng YG (2016) SAM/SAH analogs as versatile tools for SAM-dependent methyltransferases. ACS Chem Biol 11:583
Zheng W, Ibáñez G, Wu H, Blum G, Zeng H, Dong A, Li F, Hajian T, Allali-Hassani A, Amaya MF, Siarheyeva A, Yu W, Brown PJ, Schapira M, Vedadi M, Min J, Luo M (2012) Sinefungin derivatives as inhibitors and structure probes of protein lysine methyltransferase SETD2. J Am Chem Soc 134:18004
Fernández-de Gortari E, Medina-Franco JL (2015) Epigenetic relevant chemical space: a chemoinformatic characterization of inhibitors of DNA methyltransferases. RSC Adv 5:87465
Marzag H, Warnault P, Bougrin K, Martinet N, Benhida R (2014) Natural polyphenols as potent inhibitors of DNA methyltransferases, 1st edn. Elsevier, Amsterdam
Maugeri A, Barchitta M, Mazzone MG, Giuliano F, Basile G, Agodi A (2018) Resveratrol modulates SIRT1 and DNMT functions and restores LINE-1 methylation levels in ARPE-19 cells under oxidative stress and inflammation. Int J Mol Sci 19:1
Aldawsari FS, Aguayo-Ortiz R, Kapilashrami K, Yoo J, Luo M, Medina-Franco JL, Velázquez-Martínez CA (2016) Resveratrol-salicylate derivatives as selective DNMT3 inhibitors and anticancer agents. J Enzyme Inhib Med Chem 31:695
Weng JR, Lai IL, Yang HC, Lin CN, Bai LY (2014) Identification of kazinol Q, a natural product from Formosan plants, as an inhibitor of DNA methyltransferase. Phytother Res 28:49
Parasuraman S (2011) Toxicological screening. J Pharmacol Pharmacother 2:74
Gleeson MP, Modi S, Bender A, Robinson RL, Kirchmair J, Promkatkaew M, Hannongbua S, Glen RC (2012) The challenges involved in modeling toxicity data in silico: a review. Curr Pharm Des 18:1266
Sosnin S, Karlov D, Tetko IV, Fedorov MV (2018) A comparative study of multitask toxicity modeling on a broad chemical space. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.8b00685
Hamadache M, Amrane A, Benkortbi O, Hanini S, Khaouane L, Moussa CS (2017) Environmental toxicity of pesticides, and its modeling by QSAR approaches, vol 471. Springer, Cham, Switzerland
Peters JU (2013) Polypharmacology – foe or friend? J Med Chem 56:8955
Maggiora G, Gokhale V (2017) A simple mathematical approach to the analysis of polypharmacology and polyspecificity data. F1000Research 6:788
Baell JB, Holloway GA (2010) 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
Stork C, Wagner J, Friedrich N-O, de Bruyn KC, Šícho M, Kirchmair J (2018) Hit Dexter: a machine-learning model for the prediction of frequent hitters. ChemMedChem 13:564
Baell JB (2016) Feeling Nature’s PAINS: natural products, natural product drugs, and Pan Assay Interference Compounds (PAINS). J Nat Prod 79:616
Arvidson KB, Valerio LG, Diaz M, Chanderbhan RF (2008) In silico toxicological screening of natural products. Toxicol Mech Methods 18:229
Onguéné PA, Simoben CV, Fotso GW, Andrae-Marobela K, Khalid SA, Ngadjui BT, Mbaze LM, Ntie-Kang F (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
Ruiz-Rodríguez MA, Vedani A, Flores-Mireles AL, Cháirez-Ramírez MH, Gallegos-Infante JA, González-Laredo RF (2017) In silico prediction of the toxic potential of lupeol. Chem Res Toxicol 30:1562
Martínez-Mayorga K, Marmolejo-Valencia AF, Cortes-Guzman F, García-Ramos JC, Sánchez-Flores EI, Barroso-Flores J, Medina-Franco JL, Esquivel-Rodriguez B (2017) Toxicity assessment of structurally relevant natural products from Mexican plants with antinociceptive activity toxicity. J Mex Chem Soc 61:186
Saldívar-González FI, Valli M, Andricopulo AD, da Silva BV, Medina-Franco JL (2019) Chemical space and diversity of the NuBBE database: a chemoinformatic characterization. J Chem Inf Model 59:74
Medina-Franco JL (2013) Chemoinformatic characterization of the chemical space and molecular diversity of compound libraries. In: Diversity-oriented synthesis. Wiley, Hoboken, NJ, p 325
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757
Saqib U, Kelley TT, Panguluri SK, Liu D, Savai R, Baig MS, Schürer SC (2018) Polypharmacology or promiscuity? Structural interactions of resveratrol with its bandwagon of targets. Front Pharmacol 9:1201
Bisson J, McAlpine JB, Friesen JB, Chen SN, Graham J, Pauli GF (2016) Can invalid bioactives undermine natural product-based drug discovery? J Med Chem 59:1671
Nelson KM, Dahlin JL, Bisson J, Graham J, Pauli GF, Walters MA (2017) The essential medicinal chemistry of curcumin. J Med Chem 60:1620
Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer S (2007) Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J Comput Aided Mol Des 21:189
Kier LD (1985) Use of the Ames test in toxicology. Regul Toxicol Pharmacol 5:59
Moura Barbosa AJ, Del Rio A (2012) Freely accessible databases of commercial compounds for high-throughput virtual screenings. Curr Top Med Chem 12:866
Clark RL, Johnston BF, Mackay SP, Breslin CJ, Robertson MN, Harvey AL (2010) The Drug Discovery Portal: a resource to enhance drug discovery from academia. Drug Discov Today 15:679
Nicola G, Liu T, Gilson MK (2012) Public domain databases for medicinal chemistry. J Med Chem 55:6987
Saldívar-González FI, Naveja JJ, Palomino-Hernández O, Medina-Franco JL (2017) Getting SMARt in drug discovery: chemoinformatics approaches for mining structure-multiple activity relationships. RSC Adv 7:632
Medina-Franco JL, Navarrete-Vázquez G, Méndez-Lucio O (2015) Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry. Future Med Chem 7:1197
Yongye AB, Medina-Franco JL (2012) Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds. J Chem Inf Model 52:2454
Chen Y, Garcia De Lomana M, Friedrich NO, Kirchmair J (2018) Characterization of the chemical space of known and readily obtainable natural products. J Chem Inf Model 58:1518
Chen CY-C (2011) TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 6:e15939
Tsai T-Y, Chang K-W, Chen CY-C (2011) iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des 25:525
Gu J, Gui Y, Chen L, Yuan G, Lu HZ, Xu X (2013) Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One 8:e62839
Valli M, dos Santos RN, Figueira LD, Nakajima CH, Castro-Gamboa I, Andricopulo AD, Bolzani VS (2013) Development of a natural products database from the biodiversity of Brazil. J Nat Prod 76:439
Pilon AC, Valli M, Dametto AC, Pinto MEF, Freire RT, Castro-Gamboa I, Andricopulo AD, Bolzani VS (2017) NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity. Sci Rep 7:7215
Ntie-Kang F, Zofou D, Babiaka SB, Meudom R, Scharfe M, Lifongo LL, Mbah JA, Mbaze LM, Sippl W, Efange SM (2013) AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS One 8:e78085
Ntie-Kang F, Onguéné PA, Scharfe M, Owono LCO, Megnassan E, Mbaze LM, Sippl W, Efange SM (2014) ConMedNP: a natural product library from central African medicinal plants for drug discovery. RSC Adv 4:409
Nguyen-Vo T-H, Le T, Pham D, Nguyen TD, Le PH, Nguyen ADT, Nguyen TD, Nguyen TN, Nguyen VA, Do HT, Trinh K, Duong HT, Le LT (2019) VIETHERB: a database for Vietnamese herbal species. J Chem Inf Model 59:1
Stratton CF, Newman DJ, Tan DS (2015) Cheminformatic comparison of approved drugs from natural product versus synthetic origins. Bioorg Med Chem Lett 25:4802
Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752
Lovering F (2013) Escape from flatland 2: complexity and promiscuity. Med Chem Commun 4:515
Chen J, Li W, Yao H, Xu J (2015) Insights into drug discovery from natural products through structural modification. Fitoterapia 103:231
Kumar SV, Saravanan D, Kumar B, Jayakumar A (2014) An update on prodrugs from natural products. Asian Pac J Trop Med 7:S54
Schäfer T, Kriege N, Humbeck L, Klein K, Koch O, Mutzel P (2017) Scaffold Hunter: a comprehensive visual analytics framework for drug discovery. J Cheminform 9:28
Rodrigues T (2017) Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point. Org Biomol Chem 15:9275
Medina-Franco J, Martinez-Mayorga K, Giulianotti M, Houghten RA, Pinilla C (2008) Visualization of the chemical space in drug discovery. Curr Comput-Aided-Drug Des 4:322
Fitzgerald SH, Sabat M, Geysen HM (2006) Diversity Space and its application to library selection and design. J Chem Inf Model 46:1588
Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inform 30:20
López-Vallejo F, Giulianotti MA, Houghten RA, Medina-Franco JL (2012) Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today 17:718
Chen H, Engkvist O, Blomberg N, Li J (2012) A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds. MedChemCommun 3:312
Pascolutti M, Campitelli M, Nguyen B, Pham N, Gorse AD, Quinn RJ (2015) Capturing Nature’s diversity. PLoS One 10:e0120942
Pilón-Jiménez BA, Saldívar-González FI, Díaz-Eufracio BI, Medina-Franco JL (2019) BIOFACQUIM: a Mexican compound database of natural products. Biomolecules 9(1):31
González-Medina M, Prieto-Martínez FD, Owen JR, Medina-Franco JL (2016) Consensus diversity plots: a global diversity analysis of chemical libraries. J Cheminform 8:63
González-Medina M, Owen JR, El-Elimat T, Pearce CJ, Oberlies NH, Figueroa M, Medina-Franco JL (2017) Scaffold diversity of fungal metabolites. Front Pharmacol 8:180
Olmedo DA, González-Medina M, Gupta MP, Medina-Franco JL (2017) Cheminformatic characterization of natural products from Panama. Mol Divers 21:779
Naveja JJ, Rico-Hidalgo MP, Medina-Franco JL (2018) Analysis of a large food chemical database: chemical space, diversity, and complexity. F1000Research 7:993
Medina-Franco JL, Martínez-Mayorga K, Bender A, Scior T (2009) Scaffold diversity analysis of compound datasets using an entropy-based measure. QSAR Comb Sci 28:1551
González-Medina M, Prieto-Martínez FD, Naveja JJ, Méndez-Lucio O, El-Elimat T, Pearce CJ, Oberlies NH, Figueroa M, Medina-Franco JL (2016) Chemoinformatic expedition of the chemical space of fungal products. Future Med Chem 06:1113
Acknowledgments
Fernando Prieto-Martínez is grateful for a Ph.D. scholarship from the Consejo Nacional de Ciencia y Tecnología (CONACyT) No. 660465/576637. The authors also thank the Programa de Nuevas Alternativas de Tratamiento para Enfermedades Infecciosas (NUATEI-IIB-UNAM). José Medina-Franco acknowledges the School of Chemistry of the Universidad Nacional Autónoma de México (UNAM), the Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) grant number IA203718, UNAM and the Consejo Nacional de Ciencia y Tecnología grant number 282785. Fernando Prieto-Martínez and José Medina-Franco also thank Dirección General de Cómputo y de Tecnologías de Información y Comunicación (DGTIC), project grant LANCAD-UNAM-DGTIC-335 for the computational resources to use Miztli supercomputer at UNAM. The authors thank Fernanda I. Saldívar-González for providing the datasets on natural products used to compute the toxicity profile, Dr. Sharon Luna for assisting in the analysis of the toxicity data, and Edgar López-López for helpful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Prieto-Martínez, F.D., Norinder, U., Medina-Franco, J.L. (2019). Cheminformatics Explorations of Natural Products. In: Kinghorn, A., Falk, H., Gibbons, S., Kobayashi, J., Asakawa, Y., Liu, JK. (eds) Progress in the Chemistry of Organic Natural Products 110. Progress in the Chemistry of Organic Natural Products, vol 110. Springer, Cham. https://doi.org/10.1007/978-3-030-14632-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-14632-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14631-3
Online ISBN: 978-3-030-14632-0
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)