Abstract
Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.
Acknowledgements
FNK acknowledges a Georg Forster return fellowship from the Alexander von Humboldt Foundation, Germany. MV and VSB gratefully acknowledge financial support from FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo [grant number 2010/52327-5], [grant number 2013/07600-3]; CNPq (Conselho Nacional de DesenvolvimentoCientífico e Tecnológico) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). MV acknowledges from Finatec [grant number 120/2017]. The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the article. The authors are also grateful to Mr Conrad V. Simoben for helping in a number of articles. Financial support for this work is acknowledged from a ChemJets fellowship from the Ministry of Education, Youth and Sports of the Czech Republic awarded to FNK.
References and Further Reading
[1] Lusher SJ, McGuire R, Schaik van RC, Nicholson CD, Vlieg de J. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today. 2014;19:859–68.10.1016/j.drudis.2013.12.004Suche in Google Scholar PubMed
[2] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res. 2000;28:235–42.10.1093/nar/28.1.235Suche in Google Scholar PubMed PubMed Central
[3] Ertl P. Molecular structure input on the web. J Cheminform. 2010;2:1.10.1186/1758-2946-2-1Suche in Google Scholar PubMed PubMed Central
[4] Gasteiger J. Chemoinformatics: achievements and challenges, a personal view. Molecules. 2016;21:151.10.3390/molecules21020151Suche in Google Scholar PubMed PubMed Central
[5] Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:D1100–7.10.1093/nar/gkr777Suche in Google Scholar PubMed PubMed Central
[6] Jónsdóttir SO, Jørgensen FS, Brunak S. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates. Bioinformatics. 2005;21:2145–60.10.1093/bioinformatics/bti314Suche in Google Scholar PubMed
[7] NCI Open Database website. Available at: https://cactus.nci.nih.gov/download/nci/. Accessed: 6 Oct 2018.Suche in Google Scholar
[8] PubMed. Available at: https://www.ncbi.nlm.nih.gov/pubmed/. Accessed: 30 Dec 2017.Suche in Google Scholar
[9] DAYLIGHT. Available at: http://www.daylight.com/dayhtml/doc/theory/theory.smiles.html. Accessed: 29 Dec 2017.Suche in Google Scholar
[10] Kristensen TG, Nielsen J, Pedersen CNS. Methods for similarity-based virtual screening. Comput Struct Biotechnol J. 2013;5:201302009.10.5936/csbj.201302009Suche in Google Scholar PubMed PubMed Central
[11] O’Boyle NM. Towards a Universal SMILES representation–A standard method to generate canonical SMILES based on the InChI. J Cheminform. 2012;4:22.10.1186/1758-2946-4-22Suche in Google Scholar PubMed PubMed Central
[12] PubChem. Available at: https://pubchem.ncbi.nlm.nih.gov/. Accessed: 20 Dec 2017.Suche in Google Scholar
[13] Heller S, McNaught A, Stein S, Tchekhovskoi D, Pletnev I. InChI-the worldwide chemical structure identifier standard. J Cheminform. 2013;5:7.10.1186/1758-2946-5-7Suche in Google Scholar PubMed PubMed Central
[14] Chepelev LL, Dumontier M. Chemical entity semantic specification: knowledge representation for efficient semantic cheminformatics and facile data integration. J Cheminform. 2011;3:20.10.1186/1758-2946-3-20Suche in Google Scholar PubMed PubMed Central
[15] IUPAC Gold Book. Available at: https://goldbook.iupac.org/html/M/MT06966.html. Accessed: 30 Dec 2017.Suche in Google Scholar
[16] IUPAC. Compendium of chemical terminology, 2nd ed. (the “Gold Book”). Compiled by A. D. McNaught and A. Wilkinson. Blackwell Scientific Publications, Oxford (1997). XML on-line corrected version: http://goldbook.iupac.org (2006-) created by M. Nic, J. Jirat, B. Kosata; updates compiled by A. Jenkins. ISBN 0-9678550-9-8.Suche in Google Scholar
[17] PDB Guide website. Available at: http://pdb101.rcsb.org/learn/guide-to-understanding-pdb-data/introduction. Accessed: 31 Dec 2017.Suche in Google Scholar
[18] Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, et al. Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model. 2012;52:867–81.10.1021/ci200528dSuche in Google Scholar PubMed
[19] Wang L, Xie XQ. Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery?. Future Med Chem. 2014;6:247–9.10.4155/fmc.14.5Suche in Google Scholar PubMed PubMed Central
[20] Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012;4:17.10.1186/1758-2946-4-17Suche in Google Scholar PubMed PubMed Central
[21] ChemDraw/Cambridgesoft. Available at: http://www.cambridgesoft.com/software/overview.aspx. Accessed: 31 Dec 2017.Suche in Google Scholar
[22] Schrodinger website. Available at: https://www.schrodinger.com/maestro. Accessed: 31 Dec 2017.Suche in Google Scholar
[23] ChemSketch//ACD/Labs. Available at: http://www.acdlabs.com/resources/freeware/chemsketch/. Accessed: 31 Dec 2017.Suche in Google Scholar
[24] MarvinSketch/ChemAxon. Available at: https://www.chemaxon.com/products/marvin. Accessed: 31 Dec 2017.Suche in Google Scholar
[25] Bienfait B, Ertl P. JSME: a free molecule editor in JavaScript. J Cheminform. 2013;5:24.10.1186/1758-2946-5-24Suche in Google Scholar PubMed PubMed Central
[26] JSME. NA. http://peter-ertl.com/jsme/JSME_2017-02-26/JSME.html. JanJan 20172017 Available at. Accessed: 1Jan2017.Suche in Google Scholar
[27] O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform. 2011;3:33.10.1186/1758-2946-3-33Suche in Google Scholar PubMed PubMed Central
[28] Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E. The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J Chem Inf Comput Sci. 2003;43:493–500.10.1021/ci025584ySuche in Google Scholar PubMed PubMed Central
[29] Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N. The chemistry development kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform. 2017;9:33.10.1186/s13321-017-0220-4Suche in Google Scholar PubMed PubMed Central
[30] RDKit Documentation. Available at: https://www.rdkit.org/RDKit_Docs.current.pdf, Greg Landrum, Release 2018.03.1/. Accessed: 2 Oct 2018.Suche in Google Scholar
[31] Moura Barbosa AJ, Del Rio A. Freely accessible databases of commercial compounds for high-throughput virtual screenings. Curr Top Med Chem. 2012;12:866–77.10.2174/156802612800166710Suche in Google Scholar PubMed
[32] Lionta E, Spyrou G, Vassilatis DK, Cournia Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem. 2014;14:1923–38.10.2174/1568026614666140929124445Suche in Google Scholar PubMed PubMed Central
[33] Isberg V, Mordalski S, Munk C, Rataj K, Harpsøe K, Hauser AS, et al. GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res. 2016;44:D356–64.10.1093/nar/gkv1178Suche in Google Scholar PubMed PubMed Central
[34] Isberg V, Mordalski S, Munk C, Rataj K, Harpsøe K, Hauser AS, et al. Corrigendum: GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res. 2017;45:2936.10.1093/nar/gkw1218Suche in Google Scholar
[35] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997;23:3–25.10.1016/S0169-409X(96)00423-1Suche in Google Scholar
[36] Prachayasittikul V, Worachartcheewan A, Shoombuatong W, Songtawee N, Simeon S, Prachayasittikul V, et al. Computer-aided drug design of bioactive natural products. Curr Top Med Chem. 2015;15:1780–800.10.2174/1568026615666150506151101Suche in Google Scholar PubMed
[37] Walters WP, Stahl MT, Murcko MA. Virtual screening-an overview. Drug Discov Today. 1998;3:160–78.10.1016/S1359-6446(97)01163-XSuche in Google Scholar
[38] Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today. 2010;15:444–50.10.1016/j.drudis.2010.03.013Suche in Google Scholar PubMed
[39] Lee CH, Huang HC, Juan HF. Reviewing ligand-based rational drug design: the search for an atp synthase inhibitor. Int J Mol Sci. 2011;12:5304–18.10.3390/ijms12085304Suche in Google Scholar PubMed PubMed Central
[40] Sebastián-Pérez V, Roca C, Awale M, Reymond JL, Martinez A, Gil C, et al. Medicinal and biological chemistry (MBC) library: an efficient source of new hits. J Chem Inf Model. 2017;57:2143−51.10.1021/acs.jcim.7b00401Suche in Google Scholar PubMed
[41] Akhondi SA, Kors JA, Muresan S. Consistency of systematic chemical identifiers within and between small-molecule databases. J Chem. 2012;4:35.10.1186/1758-2946-4-35Suche in Google Scholar PubMed PubMed Central
[42] Ntie-Kang F, Mbah JA, Mbaze LM, Lifongo LL, Scharfe M, Hanna JN, et al. CamMedNP: building the Cameroonian 3D structural natural products database for virtual screening. BMC Complement Alterna Med. 2013;13:88.10.1186/1472-6882-13-88Suche in Google Scholar PubMed PubMed Central
[43] Bioclipse. Available at: http://www.bioclipse.net/. Accessed: 5 Jan 2018.Suche in Google Scholar
[44] FAFDrugs4. Available at: http://fafdrugs3.mti.univ-paris-diderot.fr/. Accessed: 5 Jan 2018.Suche in Google Scholar
[45] E-Dragon. Available at: http://www.vcclab.org/lab/edragon/start.html. Accessed: 5 Jan 2018.Suche in Google Scholar
[46] Molinspiration. Available at: http://www.molinspiration.com/. Accessed: 5 Jan 2018.Suche in Google Scholar
[47] VLS3D.COM. Available at: http://www.vls3d.com/. Accessed: 9 Jan 2018.Suche in Google Scholar
[48] MOE (Chemical Computing Group). Available at: http://www.chemcomp.com/index.htm. Accessed: 4 Jan 2018.Suche in Google Scholar
[49] Forli S. Charting a path to success in virtual screening. Molecules. 2015;20:18732–58.10.3390/molecules201018732Suche in Google Scholar PubMed PubMed Central
[50] Irwin JJ, Shoichet BK. ZINC - a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005;45:177–82.10.1021/ci049714+Suche in Google Scholar PubMed
[51] Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A free tool to discover chemistry for biology. J Chem Inf Model. 2012;52:1757–68.10.1021/ci3001277Suche in Google Scholar PubMed
[52] Sterling T, Irwin JJ. ZINC 15−ligand discovery for everyone. J Chem Inf Model. 2015;55:2324−37.10.1021/acs.jcim.5b00559Suche in Google Scholar PubMed
[53] Bruns RF, Watson IA. Rules for identifying potentially reactive or promiscuous compounds. J Med Chem. 2012;55:9763–72.10.1021/jm301008nSuche in Google Scholar PubMed
[54] Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719–40.10.1021/jm901137jSuche in Google Scholar PubMed
[55] Bisson J, McAlpine JB, Friesen JB, Chen SN, Graham J, Pauli GF. Can invalid bioactives undermine natural product-based drug discovery? J Med Chem. 2016;59:1671−90.10.1021/acs.jmedchem.5b01009Suche in Google Scholar PubMed
[56] Pouliot M, Jeanmart S. Pan assay interference compounds (PAINS) and other promiscuous compounds in antifungal research. J Med Chem. 2016;59:497–503.10.1021/acs.jmedchem.5b00361Suche in Google Scholar PubMed
[57] Saubern S, Guha R, Baell JB. KNIME workflow to assess PAINS filters in SMARTS format. Comparison of RDKit and Indigo cheminformatics libraries. Mol Inf. 2011;30:847–50.10.1002/minf.201100076Suche in Google Scholar
[58] Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods. 2000;44:235–49.10.1016/S1056-8719(00)00107-6Suche in Google Scholar PubMed
[59] Lagorce D, Sperandio O, Baell JB, Miteva MA, Villoutreix BO. FAF-Drugs3: a web server for compound property calculation and chemical library design. Nucleic Acids Res. 2015;43:W200–7.10.1093/nar/gkv353Suche in Google Scholar PubMed PubMed Central
[60] Douguet D. e-LEA3D: a computational-aided drug design web server. Nucleic Acids Res. 2010;38:W615–21.10.1093/nar/gkq322Suche in Google Scholar PubMed PubMed Central
[61] Molsoft. Available at: http://www.molsoft.com/chemical-library.html. Accessed: 8 Jan 2018.Suche in Google Scholar
[62] Sud M, Fahy E, Subramaniam S. Template-based combinatorial enumeration of virtual compound libraries for lipids. J Cheminform. 2012;4:23.10.1186/1758-2946-4-23Suche in Google Scholar PubMed PubMed Central
[63] Inte:Ligand. Available at: http://www.inteligand.com/. Accessed: 7 Jan 2018.Suche in Google Scholar
[64] Choi H, Cho SY, Pak HJ, Kim Y, Choi JY, Lee YJ, et al. NPCARE: database of natural products and fractional extracts for cancer regulation. J Cheminform. 2017;9:2.10.1186/s13321-016-0188-5Suche in Google Scholar PubMed PubMed Central
[65] Gu J, Gui Y, Chen L, Yuan G, Lu HZ, Xu X. Use of natural products as chemical library for drug discovery and network pharmacology. PLoS ONE. 2013;8:e62839.10.1371/journal.pone.0062839Suche in Google Scholar PubMed PubMed Central
[66] Ntie-Kang F, Amoa Onguéné P, Fotso GW, Andrae-Marobela K, Bezabih M, Ndom JC, et al. Virtualizing the p-ANAPL library: a step towards drug discovery from African medicinal plants. PLoS ONE. 2014;9:e90655.10.1371/journal.pone.0090655Suche in Google Scholar PubMed PubMed Central
[67] Atanasov AG, Waltenberger B, Pferschy-Wenzig EM, Linder T, Wawrosch C, Uhrin P, et al. Discovery and resupply of pharmacologically active plant-derived natural products: A review. Biotechnol Adv. 2015;33:1582–614.10.1016/j.biotechadv.2015.08.001Suche in Google Scholar PubMed PubMed Central
[68] Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem. 1998;70:1129–43. https://www.iupac.org/publications/pac/pdf/1998/pdf/7005x1129.pdf. Accessed: 23 Jan 2018.10.1351/pac199870051129Suche in Google Scholar
[69] Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol. 2007;152:9–20.10.1038/sj.bjp.0707305Suche in Google Scholar PubMed PubMed Central
[70] Kaserer T, Temml V, Kutil Z, Vanek T, Landa P, Schuster D. Prospective performance evaluation of selected common virtual screening tools. Case study: cyclooxygenase (COX) 1 and 2. Eur J Med Chem. 2015;96:445–57.10.1016/j.ejmech.2015.04.017Suche in Google Scholar PubMed PubMed Central
[71] Klenner A, Hähnke V, Geppert T, Schneider P, Zettl H, Haller S, et al. From virtual screening to bioactive compounds by visualizing and clustering of chemical space. Mol Inf. 2012;31:21–26.10.1002/minf.201100147Suche in Google Scholar PubMed
[72] Noha SM, Jassar B, Kuehnl S, Rollinger JM, Stuppner H, Schaible AM, et al. Pharmacophore-based discovery of a novel cytosolic phospholipase A2α inhibitor. Bioorg Med Chem Lett. 2012;22:1202–7.10.1016/j.bmcl.2011.11.093Suche in Google Scholar PubMed PubMed Central
[73] ChemBioServer. Available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/. Accessed: 9 Jan 2018.Suche in Google Scholar
[74] Athanasiadis E, Cournia Z, Spyrou G. ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics. 2012;28:3002–3.10.1093/bioinformatics/bts551Suche in Google Scholar PubMed
[75] NuBBE database (NuBBEDB). Available at: http://nubbe.iq.unesp.br/portal/nubbedb.html. Accessed: 21 Dec 2017.Suche in Google Scholar
[76] ChEMBL. Available at: https://www.ebi.ac.uk/chembl/. Accessed: 7 Jan 2018.Suche in Google Scholar
[77] Banerjee P, Erehman J, Gohlke BO, Wilhelm T, Preissner R, Dunkel M. Super Natural II-a database of natural products. Nucleic Acids Res. 2015;43:D935–9.10.1093/nar/gku886Suche in Google Scholar PubMed PubMed Central
[78] CACTVS. Available at: http://xemistry.com/. Accessed: 9 Jan 2018.Suche in Google Scholar
[79] Instant JChem (IJC). Available at: https://chemaxon.com/products/instant-jchem. Accessed: 9 Jan 2018.Suche in Google Scholar
[80] CORINA Symphony. Available at: https://www.mn-am.com/products/corinasymphony. Accessed: 9 Jan 2018.Suche in Google Scholar
[81] Screening Assistant 2. Available at: http://sa2.sourceforge.net/. Accessed: 9 Jan 2018.Suche in Google Scholar
[82] The RDKit Database Cartridge: Open-source Cheminformatics and Machine Learning. Available at: http://www.rdkit.org/docs/Cartridge.html. Accessed: 10 Jan 2018.Suche in Google Scholar
[83] BIOVIA Chemical Registration. Available at: http://accelrys.com/products/collaborative-science/biovia-registration/chemical-registration.html. Accessed: 10 Jan 2018.Suche in Google Scholar
[84] ChemCurator. Available at: https://chemaxon.com/products/chemcurator. Accessed: 10 Jan 2018.Suche in Google Scholar
[85] Hersey A, Chambers J, Bellis L, Bento AP, Gaulton A, Overington JP. Chemical databases: curation or integration by user-defined equivalence?. Drug Discov Today. 2015;14:17–24.10.1016/j.ddtec.2015.01.005Suche in Google Scholar PubMed PubMed Central
[86] UniChem. Available at: https://www.ebi.ac.uk/unichem/widesearch/widesearch. Accessed: 10 Jan 2018.Suche in Google Scholar
[87] CoCoCo. Available at: http://cococo.isof.cnr.it/cococo. Accessed: 6 Jan 2018.Suche in Google Scholar
[88] DrugBank. Available at: https://www.drugbank.ca/. Accessed: 6 Jan 2018.Suche in Google Scholar
[89] BindingDB. Available at: https://www.bindingdb.org/bind/index.jsp. Accessed: 6 Jan 2018.Suche in Google Scholar
[90] ChemSpider. Available at: http://www.chemspider.com/Default.aspx. Accessed: 6 Jan 2018.Suche in Google Scholar
[91] ChemBank. Available at: http://chembank.broadinstitute.org/. Accessed: 6 Jan 2018.Suche in Google Scholar
[92] ChEBI. Available at: http://www.ebi.ac.uk/chebi/. Accessed: 25 Dec 2017.Suche in Google Scholar
[93] Binding MOAD. Available at: http://www.bindingmoad.org/. Accessed: 6 Jan 2018.Suche in Google Scholar
[94] eMolecules. Available at: https://www.emolecules.com/info/plus/download-database. Accessed: 10 Jan 2018.Suche in Google Scholar
[95] ZINC15 database. Available at: http://zinc15.docking.org/. Accessed: 20 Dec 2017.Suche in Google Scholar
[96] Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42:D1083–90.10.1093/nar/gkt1031Suche in Google Scholar PubMed PubMed Central
[97] Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 2017;45:D271–81.Suche in Google Scholar PubMed
[98] GVKBIO. Available at: https://www.gvkbio.com/. Accessed: 7 Jan 2018.Suche in Google Scholar
[99] Reaxys-Fact Sheet. Available at: https://www.elsevier.com/solutions/reaxys. Accessed: 19 Dec 2017.Suche in Google Scholar
[100] Chackalamannil S, Davies RJ, Asberom T, Doller D, Leone D. A highly efficient total synthesis of (+)-Himbacine. J Am Chem Soc. 1996;118:9812–3.10.1021/ja962542fSuche in Google Scholar
[101] Chen Y, Kops de Bruyn C, Kirchmair J. Data resources for the computer-guided discovery of bioactive natural products. J Chem Inf Model. 2017;57:2099−111.10.1021/acs.jcim.7b00341Suche in Google Scholar PubMed
[102] INsPiRE workshop: Cell cycle and natural products, book of abstracts: Kinghorn AD. Discovery of anticancer agents of diverse natural origin. Athens, Greece: 2014 May 8–9.Suche in Google Scholar
[103] Dictionary of Natural Products (DNP). Available at: http://dnp.chemnetbase.com. Accessed: 17 Dec 2017.Suche in Google Scholar
[104] Dictionary of Marine Natural Products (DMNP). Available at: http://dmnp.chemnetbase.com. Accessed: 18 Dec 2017.Suche in Google Scholar
[105] Super Natural II. Available at: http://bioinf-applied.charite.de/supernatural_new. Accessed: 18 Dec 2017.Suche in Google Scholar
[106] Chen CY-C. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS ONE. 2011;6:15939.10.1371/journal.pone.0015939Suche in Google Scholar PubMed PubMed Central
[107] TCM Database@Taiwan. Available at: http://tcm.cmu.edu.tw/. Accessed: 18 Dec 2017.Suche in Google Scholar
[108] AfroDb. Available at: http://african-compounds.org/about/afrodb/. Accessed 19 Dec 2017.Suche in Google Scholar
[109] Ntie-Kang F, Zofou D, Babiaka SB, Meudom R, Scharfe M, Lifongo LL, et al. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS ONE. 2013;8:e78085.10.1371/journal.pone.0078085Suche in Google Scholar PubMed PubMed Central
[110] Northern African Natural Products Database. Available at: http://african-compounds.org/nanpdb/. Accessed: 20 Dec 2017.Suche in Google Scholar
[111] Ntie-Kang F, Telukunta KK, Döring K, Simoben CV, Moumbock AFA, Malange YI, et al. NANPDB: A resource for natural products from Northern African sources. J Nat Prod. 2017;80:2067–76.10.1021/acs.jnatprod.7b00283Suche in Google Scholar PubMed
[112] Xue R, Fang Z, Zhang M, Yi Z, Wen C, Shi T. TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013;41:D1089–95.10.1093/nar/gks1100Suche in Google Scholar PubMed
[113] TCMID-Traditional Chinese Medicines Integrated Database. Available at: www.megabionet.org/tcmid. Accessed: 19 Dec 2017.Suche in Google Scholar
[114] Reaxys Training, Natural Products in Reaxys (Christine Flemming). Available at: https://www.youtube.com/watch?v=vJKXsDDhRyk Accessed: 19 Dec 2017.Suche in Google Scholar
[115] AfroCancer. Available at: http://african-compounds.org/about/afrocancer/. Accessed: 19 Dec 2017.Suche in Google Scholar
[116] Ntie-Kang F, Nwodo JN, Ibezim A, Simoben CV, Karaman B, Ngwa VF, et al. Molecular modeling of potential anticancer agents from African medicinal plants. J Chem Inf Model. 2014;54:2433–50.10.1021/ci5003697Suche in Google Scholar PubMed
[117] Chem-TCM: Chemical Database of Traditional Chinese Medicine. Available at: http://www.chemtcm.com/. Accessed: 19 Dec 2017.Suche in Google Scholar
[118] Hao M, Cheng T, Wang Y, Bryant SH. Web search and data mining of natural products and their bioactivities in PubChem. Sci China Chem. 2013;56:1424–35.10.1007/s11426-013-4910-0Suche in Google Scholar
[119] Lin YC, Wang CC, Chen IS, Jheng JL, Li JH, Tung CW. TIPdb: a database of anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan. Sci World J. 2013;Article ID 736386.10.1155/2013/736386Suche in Google Scholar
[120] TIPdb-The Taiwan indigenous plant database. Available at: http://cwtung.kmu.edu.tw/tipdb/. Accessed: 20 Dec 2017.Suche in Google Scholar
[121] Tung CW, Lin YC, Chang HS, Wang CC, Chen IS, Jheng JL, et al. TIPdb-3D: the three-dimensional structure database of phytochemicals from Taiwan indigenous plants. Database. 2014;2014:Article ID bau055.10.1093/database/bau055Suche in Google Scholar
[122] Hatherley R, Brown DK, Musyoka TM, Penkler DL, Faya N, Lobb KA, et al. SANCDB: a South African natural compound database. J Cheminform. 2015;7:29.10.1186/s13321-015-0080-8Suche in Google Scholar PubMed
[123] South African Natural Compounds Database (SANCDB). Available at: https://sancdb.rubi.ru.ac.za/. Accessed: 20 Dec 2017.Suche in Google Scholar
[124] Carotenoids Database. Available at: www.carotenoiddb.jp. Accessed: 21 Dec 2017.Suche in Google Scholar
[125] Yabuzaki J. Carotenoids database: structures, chemical fingerprints and distribution among organisms. Database. 2017;2017:Article ID bax004.10.1093/database/bax004Suche in Google Scholar
[126] Bolzani VS, Castro-Gamboa I, Silva DHS. In comprehensive natural products II chemistry and biology; Verpoorte R, Editor. Oxford-UK: Elsevier. Vol. 3, Chapter 3.05, pp. 95–133 2010.Suche in Google Scholar
[127] Pagotto CLAC, Barros JRT, Borin MRMB, Gottlieb OR. Quantitative chemical biology. II. Chemical mapping of Lauraceae. Anais da Academia Brasileira de Ciências. 1998;70:705–9.Suche in Google Scholar
[128] Silva DHS, Cavalheiro AJ, Yoshida M, Gottlieb OR. The chemistry of Brazilian Myristicaceae. Xxxvii. Flavonolignoids from the fruits of Iryanthera grandis. Phytochemistry. 1995;38:1013–6.10.1016/0031-9422(94)00730-HSuche in Google Scholar
[129] Kato MJ, Yoshida M, Gottlieb OR. The chemistry of Brazilian Myristicaceae.34. Flavones and lignans in flowers, fruits and seedlings of Virola venenosa. Phytochemistry. 1992;31:283–7.10.1016/0031-9422(91)83055-PSuche in Google Scholar
[130] Cabral MMO, Azambuja P, Gottlieb OR, Garcia ES. Effects of some lignans and neolignans on the development and excretion of Rhodnius prolixus. Fitoterapia. 2000;71:1–9.10.1016/S0367-326X(99)00105-7Suche in Google Scholar PubMed
[131] Marques MOM, Yoshida M, Gottlieb OR, Maia JGS. The chemistry of Brazilian Lauraceae.97. Neolignans from Licaria aurea. Phytochemistry. 1992;31:360–1.10.1016/0031-9422(91)83079-ZSuche in Google Scholar
[132] Medina RP, Silva AD, Andersen RJ, Araújo AR, Silva DHS. Botryane sesquiterpenes and binaphthalene tetrols from endophytic fungi associated to the marine red algae Asparagopsis taxiformis. Planta Medica. 2016;82:S1–S381.10.1055/s-0036-1596629Suche in Google Scholar
[133] Jasandrade T, Somensi A, Lopes MN, Araújo AR, Jaspars M, Silva DH. Citrinadin A derivatives from Penicillium citrinum, an endophyte from the marine red alga Dichotomaria marginata. Planta Med. 2014;80:776–76.10.1055/s-0034-1382419Suche in Google Scholar
[134] Pinto MEF, Najas JZG, Magalhães LG, Bobey AF, Mendonça JN, Lopes NP, et al. Inhibition of breast cancer cell migration by cyclotides isolated from Pombalia calceolaria. J Nat Prod. 2018;81:1203–8.10.1021/acs.jnatprod.7b00969Suche in Google Scholar PubMed PubMed Central
[135] Pinto MEF, Batista JM, Koehbach J, Gaur P, Sharma A, Nakabashi M, et al. Ribifolin, an orbitide from Jatropha ribifolia, and its potential antimalarial activity. J Nat Prod. 2015;78:374–80.10.1021/np5007668Suche in Google Scholar PubMed
[136] Ramalho SD, Pinto MEF, Ferreira D, Bolzani VS. Biologically active orbitides from the Euphorbiaceae family. Planta Med. 2018;84:558–67.10.1055/s-0043-122604Suche in Google Scholar PubMed
[137] Valli M, dos Santos RN, Figueira LD, Nakajima CH, Castro-Gamboa I, Andricopulo AD, et al. Development of a natural products database from the biodiversity of Brazil. J Nat Prod. 2013;76:439–44.10.1021/np3006875Suche in Google Scholar PubMed
[138] Pilon AC, Valli M, Dametto AC, Pinto MEF, Freire RT, Castro-Gamboa I, et al. NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity. Sci Rep. 2017;7:7215.10.1038/s41598-017-07451-xSuche in Google Scholar PubMed PubMed Central
[139] Villoutreix BO, Lagorce D, Labbé CM, Sperandio O, Miteva MA. One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade. Drug Discov Today. 2013;18:1081–9.10.1016/j.drudis.2013.06.013Suche in Google Scholar PubMed
[140] Harvey AL, Edrada-Ebel RA, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov. 2015;14:111–29.10.1038/nrd4510Suche in Google Scholar PubMed
[141] Kuenemann MA, Labbé CM, Cerdan AH, Sperandio O. Imbalance in chemical space: how to facilitate the identification of protein-protein interaction inhibitors. Sci Rep. 2016;6:23815.10.1038/srep23815Suche in Google Scholar PubMed PubMed Central
[142] Tietz JI, Mitchell DA. Using genomics for natural product structure elucidation. Curr Top Med Chem. 2016;16:1645–94.10.2174/1568026616666151012111439Suche in Google Scholar PubMed
[143] Mohamed A, Nguyen CH, Mamitsuka H. Current status and prospects of computational resources for natural product dereplication: a review. Brief Bioinform. 2016;17:309–21.10.1093/bib/bbv042Suche in Google Scholar PubMed
[144] Valli M, Altei W, Santos RN, Lucca Jr. EC, Dessoy MA, Pioli RM, et al. Synthetic analogue of the natural product piperlongumine as a potent inhibitor of breast cancer cell line migration. J Braz Chem Soc. 2017;28:475–84.10.21577/0103-5053.20160303Suche in Google Scholar
[145] Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, et al. HIT: linking herbal active ingredients to targets. Nucleic Acids Res. 2011;39:D1055–59.10.1093/nar/gkq1165Suche in Google Scholar PubMed PubMed Central
[146] AfroMalariaDB: African Antimalarial Natural Products Library. Available at: http://african-compounds.org/about/afromalariadb/. Accessed: 24 Dec 2017.Suche in Google Scholar
[147] Onguéné PA, Ntie-Kang F, Mbah JA, Lifongo LL, Ndom JC, Sippl W, et al. The potential of anti-malarial compounds derived from African medicinal plants, part III: an in silico evaluation of drug metabolism and pharmacokinetics profiling. Org Med Chem Lett. 2014;4:6.10.1186/s13588-014-0006-xSuche in Google Scholar PubMed PubMed Central
[148] Kang H, Tang K, Liu Q, Sun Y, Huang Q, Zhu R, et al. HIM-herbal ingredients in-vivo metabolism database. J Cheminform. 2013;5:28.10.1186/1758-2946-5-28Suche in Google Scholar PubMed PubMed Central
[149] UEFS Natural Products Database. Available at: http://zinc.docking.org/catalogs/uefsnp. Accessed: 21 Dec 2017.Suche in Google Scholar
[150] Mangal M, Sagar P, Singh H, Raghava GPS, Agarwal SM. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res. 2013;41:D1124–9.10.1093/nar/gks1047Suche in Google Scholar PubMed PubMed Central
[151] NPACT. Available at: http://crdd.osdd.net/raghava/npact/. Accessed: 21 Dec 2017.Suche in Google Scholar
[152] Klementz D, Döring K, Lucas X, Telukunta KK, Erxleben A, Deubel D, et al. StreptomeDB 2.0-an extended resource of natural products produced by streptomycetes. Nucleic Acids Res. 2016;44:D509–14.10.1093/nar/gkv1319Suche in Google Scholar PubMed PubMed Central
[153] StreptomeDB 2.0. Available at: http://132.230.56.4/streptomedb2/. Acceessed: 21 Dec 2017.Suche in Google Scholar
[154] AntiBase. Available at: https://application.wiley-vch.de/stmdata/antibase.php. Accessed: 21 Dec 2017.Suche in Google Scholar
[155] MarinLit database. Available at: http://pubs.rsc.org/marinlit/. Accessed: 21 Dec 2017.Suche in Google Scholar
[156] Pathania S, Ramakrishnan SM, Bagler G. Phytochemica: a platform to explore phytochemicals of medicinal plants. Database. 2015;2015:Article ID bav075.10.1093/database/bav075Suche in Google Scholar PubMed PubMed Central
[157] Phytochemica. Available at: faculty.iiitd.ac.in/~bagler/webservers/Phytochemica/index.php. Accessed: 21 Dec 2017.Suche in Google Scholar
[158] Alkamid®. Available at: http://alkamid.ugent.be/. Accessed: 25 Dec 2017.Suche in Google Scholar
[159] Boonen J, Bronselaer A, Nielandt J, Veryser L, De Tre´ G, De Spiegeleer B. Alkamid database: chemistry, occurrence and functionality of plant N-alkylamides. J Ethnopharmacol. 2012;142:563–90.10.1016/j.jep.2012.05.038Suche in Google Scholar PubMed
[160] 3DMET. Available at: http://www.3dmet.dna.affrc.go.jp/. Accessed: 25 Dec 2017.Suche in Google Scholar
[161] Maeda MH, Kondo K. Three-dimensional structure database of natural metabolites (3DMET): a novel database of curated 3D structures. J Chem Inf Model. 2013;53:527–33.10.1021/ci300309kSuche in Google Scholar PubMed
[162] Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 2013;41:D456–63.10.1093/nar/gks1146Suche in Google Scholar PubMed PubMed Central
[163] NAPRALERT. Available at: https://www.napralert.org/. Accessed: 5 Jan 2018.Suche in Google Scholar
[164] MPD3. Available at: http://bioinform.info/. Accessed: 14 Jan 2018.Suche in Google Scholar
[165] AnalytiCon discovery. Available at: www.ac-discovery.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[166] NCI Natural Products Repository. Available at: https://dtp.cancer.gov/organization/npb/introduction.htm. Accesssed: 22 Dec 2017.Suche in Google Scholar
[167] TimTec NPL. Available at: http://www.timtec.net/natural-compound-library.html. Accessed: 22 Dec 2017.Suche in Google Scholar
[168] NPDI. Available at: http://www.npdi-us.org/collection/. Accessed: 22 Dec 2017.Suche in Google Scholar
[169] INDOFINE Chemical Company. Available at: www.indofinechemical.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[170] Ambinter. Available at: www.ambinter.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[171] Interbioscreen. Available at: www.ibscreen.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[172] TargetMol. Available at: www.targetmol.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[173] PI Chemicals. Available at: www.pipharm.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[174] Selleckchem. Available at: http://www.selleckchem.com. Accessed: 23 Dec 2017.Suche in Google Scholar
[175] NPLI. Available at: http://www.scripps.edu/shen/NPLI/npliattsri.html. Accessed: 25 Dec 2017.Suche in Google Scholar
[176] Quality Phytochemicals. Available at: http://www.qualityphytochemicals.com/. Accessed: 25 Dec 2017.Suche in Google Scholar
[177] AK Scientific. Available at: www.aksci.com. Accessed: 22 Dec 2017.Suche in Google Scholar
[178] BioAustralis. Available at: http://www.bioaustralis.com/. Accessed: 25 Dec 2017.Suche in Google Scholar
[179] MedChem Express. Available at: http://www.medchemexpress.com/. Accessed: 2 Dec 2017.Suche in Google Scholar
[180] Specs. NA. http://www.specs.net/page.php?pageid=2004111115353984&smenu=2008111411133023. JanJan 20182018 Available at. Accessed: 5Jan2018.Suche in Google Scholar
[181] Chimiothèque Nationale (CN). Available at: http://chimiotheque-nationale.cn.cnrs.fr/?Presentation,18. Accessed: 7 Jan 2018.Suche in Google Scholar
[182] Irwin JJ, Gaskins G, Sterling T, Mysinger MM, Keiser MJ. Predicted biological activity of purchasable chemical space. J Chem Inf Model. 2018;58:148–64.10.1021/acs.jcim.7b00316Suche in Google Scholar PubMed PubMed Central
[183] Lucas X, Grüning BA, Bleher S, Günther S. The purchasable chemical space: a detailed picture. J Chem Inf Model. 2015;55:915–24.10.1021/acs.jcim.5b00116Suche in Google Scholar PubMed
[184] Doak BC, Over B, Giordanetto F, Kihlberg J. Oral druggable space beyond the rule of 5: insights from drugs and clinical candidates. Chem Biol. 2014;21:1115–42.10.1016/j.chembiol.2014.08.013Suche in Google Scholar PubMed
[185] Pascolutti M, Quinn RJ. Natural products as lead structures: chemical transformations to create lead-like libraries. Drug Discov Today. 2014;19:215–21.10.1016/j.drudis.2013.10.013Suche in Google Scholar PubMed
[186] Ma DL, Chan DSH, Leung CH. Molecular docking for virtual screening of natural product databases. Chem Sci. 2011;2:1656–65.10.1039/C1SC00152CSuche in Google Scholar
[187] Pereira F, Latino DARS, Gaudêncio SP. QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery. Molecules. 2015;20:4848–73.10.3390/molecules20034848Suche in Google Scholar PubMed PubMed Central
[188] Pye CR, Bertin MJ, Lokey RS, Gerwick WH, Linington RG. Retrospective analysis of natural products provides insights for future discovery trends. Proc Natl Acad Sci USA. 2017;114:5601–6.10.1073/pnas.1614680114Suche in Google Scholar PubMed PubMed Central
[189] Shen B. A new golden age of natural products drug discovery. Cell. 2015;163:1297–300.10.1016/j.cell.2015.11.031Suche in Google Scholar PubMed PubMed Central
Baell JB. Broad coverage of commercially available lead-like screening space with fewer than 350,000 compounds. J Chem Inf Model. 2013;53:39−55.10.1021/ci300461aSuche in Google Scholar PubMed
Bajusz D, Rácz A, Héberger K. Chemical data formats, fingerprints, and other molecular descriptions for database analysis and searching. In: Chackalamannil S, Rotella DP, Ward SE, editors. Comprehensive medicinal chemistry III, vol. 3. Oxford: Elsevier, 2017: 329–78.10.1016/B978-0-12-409547-2.12345-5Suche in Google Scholar
Dias DA, Urban S, Roessner U. A historical overview of natural products in drug discovery. Metabolites. 2012;2:303–36.10.3390/metabo2020303Suche in Google Scholar PubMed PubMed Central
Finn PW, Morris GM. Shape-based similarity searching in chemical databases. Wiley Interdiscip Rev Comput Mol Sci. 2013;3:226–41.10.1002/wcms.1128Suche in Google Scholar
Lipinski CA. Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv Drug Deliv Rev. 2016;101:34–41.10.1016/j.addr.2016.04.029Suche in Google Scholar PubMed
Mitchell JBO. Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci. 2014;4:468–81.10.1002/wcms.1183Suche in Google Scholar PubMed PubMed Central
Stratton CF, Newman DJ, Tan DS. Cheminformatic comparison of approved drugs from natural product versus synthetic origins. Bioorg Med Chem Lett. 2015;25:4802–7.10.1016/j.bmcl.2015.07.014Suche in Google Scholar PubMed PubMed Central
Tao L, Zhu F, Qin C, Zhang C, Chen S, Zhang P, Zhang C, Tan C, Gao C, Chen Z, Jiang Y, Chen YZ. Clustered distribution of natural product leads of drugs in the chemical space as influenced by the privileged target-sites. Sci Rep. 2015;5:9325.10.1038/srep09325Suche in Google Scholar PubMed PubMed Central
© 2019 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Gas chromatography/mass spectrometry techniques for the characterisation of organic materials in works of art
- Computer-based techniques for lead identification and optimization I: Basics
- 10.1515/psr-2018-0155
- Polyoxometalates in photocatalysis
- A primer on natural product-based virtual screening
- Theoretical principles of Raman spectroscopy
- Secondary metabolites, their structural diversity, bioactivity, and ecological functions: An overview
- Applications in: Environmental Analytics (fine particles)
- Synthesis and characterization of size controlled bimetallic nanosponges
Artikel in diesem Heft
- Gas chromatography/mass spectrometry techniques for the characterisation of organic materials in works of art
- Computer-based techniques for lead identification and optimization I: Basics
- 10.1515/psr-2018-0155
- Polyoxometalates in photocatalysis
- A primer on natural product-based virtual screening
- Theoretical principles of Raman spectroscopy
- Secondary metabolites, their structural diversity, bioactivity, and ecological functions: An overview
- Applications in: Environmental Analytics (fine particles)
- Synthesis and characterization of size controlled bimetallic nanosponges