Planta Med 2015; 81(06): 436-449
DOI: 10.1055/s-0034-1396314
Reviews
Georg Thieme Verlag KG Stuttgart · New York

How to Valorize Biodiversity? Letʼs Go Hashing, Extracting, Filtering, Mining, Fishing

Quoc Tuan Do
1   Greenpharma S. A. S., Orléans, France
,
José L. Medina-Franco
2   Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Mexico City, Mexico
,
Thomas Scior
3   Department of Pharmacy, Benemérita Universidad Autónoma de Puebla, Puebla, México
,
Philippe Bernard
1   Greenpharma S. A. S., Orléans, France
› Author Affiliations
Further Information

Publication History

received 15 July 2014
revised 21 October 2014

accepted 09 January 2015

Publication Date:
25 February 2015 (online)

Abstract

Nature was and still is a prolific source of inspiration in pharmacy, cosmetics, and agro-food industries for the discovery of bioactive products. Informatics is now present in most human activities. Research in natural products is no exception. In silico tools may help in numerous cases when studying natural substances: in pharmacognosy, to store and structure the large and increasing number of data, and to facilitate or accelerate the analysis of natural products in regards to traditional uses of natural resources; in drug discovery, to rationally design libraries for screening natural compound mimetics and identification of biological activities for natural products. Here we review different aspects of in silico approaches applied to the research and development of bioactive substances and give examples of using nature-inspiring power and ultimately valorize biodiversity.

 
  • References

  • 1 Convention on biological diversity. Montreal: Secretariat of the Convention on Biological Diversity; 2012
  • 2 Pimm SL, Jenkins CN, Abell R, Brooks TM, Gittleman JL, Joppa LN, Raven PH, Roberts CM, Sexton JO. The biodiversity of species and their rates of extinction, distribution, and protection. Science 2014; 344: 1246752
  • 3 Rao M, Htun S, Platt SG, Tizard R, Poole C, Myint T, Watson JE. Biodiversity conservation in a changing climate: a review of threats and implications for conservation planning in Myanmar. Ambio 2013; 42: 789-804
  • 4 Camp D, Newman S, Pham NB, Quinn RJ. Nature Bank and the Queensland Compound Library: unique international resources at the Eskitis Institute for Drug Discovery. Comb Chem High Throughput Screen 2014; 17: 201-209
  • 5 http://www.ffem.fr/lang/en/accueil/activites-ffem/biodiversite_protection Accessed September 26, 2013
  • 6 http://www.marex.fi/ Accessed September 26, 2013
  • 7 Burton RA, Fincher GB. Plant cell wall engineering: applications in biofuel production and improved human health. Curr Opin Biotechnol 2014; 26: 79-84
  • 8 Huskinson B, Marshak MP, Suh C, Er S, Gerhardt MR, Galvin CJ, Chen X, Aspuru-Guzik A, Gordon RG, Aziz MJ. A metal-free organic-inorganic aqueous flow battery. Nature 2014; 505: 195-198
  • 9 Bhushan B. Biomimetics: lessons from nature–an overview. Philos Trans A Math Phys Eng Sci 2009; 367: 1445-1486
  • 10 Bernard P, Scior T, Do QT. Modulating testosterone pathway: a new strategy to tackle male skin aging?. Clin Interv Aging 2012; 7: 351-361
  • 11 Graziose R, Lila MA, Raskin I. Merging traditional Chinese medicine with modern drug discovery technologies to find novel drugs and functional foods. Curr Drug Discov Technol 2010; 7: 2-12
  • 12 Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 2012; 75: 311-335
  • 13 Berkov S, Mutafova B, Christen P. Molecular biodiversity and recent analytical developments: a marriage of convenience. Biotechnol Adv 2014; 32: 1102-1110
  • 14 Merelli I, Pérez-Sánchez H, Gesing S, DʼAgostino D. Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives. Biomed Res Int 2014; 2014: e134023
  • 15 Eid S, Zalewski A, Smieško M, Ernst B, Vedani A. A Molecular-Modeling Toolbox Aimed at Bridging the Gap between Medicinal Chemistry and Computational Sciences. Int J Mol Sci 2013; 14: 684-700
  • 16 Medina-Franco JL, Martínez-Mayorga K, Peppard TL, Del Rio A. Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products. PLoS One 2012; 7: e50798
  • 17 Blondeau S, Do QT, Scior T, Bernard P, Morin-Allory L. Reverse pharmacognosy: another way to harness the generosity of nature. Curr Pharm Des 2010; 16: 1682-1696
  • 18 Do QT, Driscoll M, Slitt A, Seeram N, Peppard TL, Bernard P. Reverse pharmacognosy: a tool to accelerate the discovery of new bioactive food ingredients. In: Martinez-Mayorga K, Medina-Franco JL, editors Food informatics: applications of chemical information to food chemistry. Heidelberg: Springer; 2014: 111-130
  • 19 Martinez-Mayorga K, Peppard TL, López-Vallejo F, Yongye AB, Medina-Franco JL. Systematic mining of Generally Recognized as Safe (GRAS) flavor chemicals for bioactive compounds. J Agric Food Chem 2013; 61: 7507-7514
  • 20 Audouze K, Tromelin A, Le Bon AM, Belloir C, Petersen RK, Kristiansen K, Brunak S, Taboureau O. Identification of odorant-receptor interactions by global mapping of the human odorome. PLoS One 2014; 9: e93037
  • 21 Bernard P, Scior T, Didier B, Hibert M, Berthon JY. Ethnopharmacology and bioinformatic combination for leads discovery: application to phospholipase A(2) inhibitors. Phytochemistry 2001; 58: 865-874
  • 22 Rollinger JM, Haupt S, Stuppner H, Langer T. Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example. J Chem Inf Comput Sci 2004; 44: 480-488
  • 23 Kerns EH. High throughput physicochemical profiling for drug discovery. J Pharm Sci 2001; 90: 1838-1858
  • 24 Avdeef A, Testa B. Physicochemical profiling in drug research: a brief survey of the state-of-the-art of experimental techniques. Cell Mol Life Sci 2002; 59: 1681-1689
  • 25 Sinko JS. Drug selection in early drug development: screening for acceptable pharmacokinetic properties using combined in vitro and computational approaches. Curr Opin Drug Discov Devel 1999; 2: 42-48
  • 26 Cartmell J, Krstajic D, Leahy DE. Competitive Workflow: novel software architecture for automating drug design. Curr Opin Drug Discov Devel 2007; 10: 347-352
  • 27 Scior T, Bernard P, Medina-Franco JL, Maggiora GM. Large compound databases for structure-activity relationships studies in drug discovery. Mini Rev Med Chem 2007; 7: 851-860
  • 28 Scior T, Medina-Franco JL, Do QT, Martínez-Mayorga K, Yunes Rojas JA, Bernard P. How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr Med Chem 2009; 16: 4297-4313
  • 29 Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52: 617-648
  • 30 Fang Y. Label-free drug discovery. Front Pharmacol 2014; 5: 1-8
  • 31 Medina-Franco JL, Giulianotti MA, Welmaker GS, Houghten RA. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov Today 2013; 18: 495-501
  • 32 Lobell M, Hendrix M, Hinzen B, Keldenich J, Meier H, Schmeck C, Schohe-Loop R, Wunberg T, Hillisch A. In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem 2006; 1: 1229-1236
  • 33 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 2001; 46: 3-26
  • 34 Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002; 45: 2615-2623
  • 35 Blake JF. Examination of the computed molecular properties of compounds selected for clinical development. Biotechniques 2003; 34: S16-S20
  • 36 Wenlock MC, Austin RP, Barton P, Davis AM, Leeson PD. A comparison of physiochemical property profiles of development and marketed oral drugs. J Med Chem 2003; 46: 1250-1256
  • 37 Palm K, Stenberg P, Luthman K, Artursson P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm Res 1997; 14: 568-571
  • 38 Clark DE. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J Pharm Sci 1999; 88: 807-814
  • 39 Ritchie TJ, Macdonald SJ. The impact of aromatic ring count on compound developability–are too many aromatic rings a liability in drug design?. Drug Discov Today 2009; 14: 1011-1020
  • 40 Di L, Kerns EH. Profiling drug-like properties in discovery research. Curr Opin Chem Biol 2003; 7: 402-408
  • 41 Cruz-Monteagudo M, Cordeiro MN. Chemoinformatics profiling of ionic liquids–uncovering structure-cytotoxicity relationships with network-like similarity graphs. Toxicol Sci 2014; 138: 191-204
  • 42 Pedretti A, Villa L, Vistoli G. VEGA: a versatile program to convert, handle and visualize molecular structure on Windows-based PCs. J Mol Graph Model 2002; 21: 47-49
  • 43 Carrió P, Pinto M, Ecker G, Sanz F, Pastor M. Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. J Chem Inf Model 2014; 54: 1500-1511
  • 44 Norinder U, Carlsson L, Boyer S, Eklund M. Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination. J Chem Inf Model 2014; 54: 1596-1603
  • 45 Moura-Barbosa AJ, Del Rio A. Freely accessible databases of commercial compounds for high-throughput virtual screenings. Curr Top Med Chem 2012; 12: 866-877
  • 46 Blunt JW, Munro MHG, Laatsch H. AntiMarin database. Christchurch: University of Canterbury. Göttingen: University of Göttingen; 2007
  • 47 Pereira F, Latino DA, Gaudêncio SP. A chemoinformatics approach to the discovery of lead-like molecules from marine and microbial sources en route to antitumor and antibiotic drugs. Mar Drugs 2014; 12: 757-778
  • 48 Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod 2007; 70: 461-477
  • 49 Newman DJ, Cragg GM, Snader KM. Natural products as sources of new drugs over the period 1981–2002. J Nat Prod 2003; 66: 1022-1037
  • 50 DiMasi JA. Pharmaceutical R&D performance by firm size: approval success rates and economic returns. Am J Ther 2014; 21: 26-34
  • 51 Bergström CA, Holm R, Jørgensen SA, Andersson SBA, Artursson P, Beato S, Borde A, Box K, Brewster M, Dressman J, Feng KI, Halbert G, Kostewicz E, McAllister M, Muenster U, Thinnes J, Taylor R, Mullertz A. Early pharmaceutical profiling to predict oral drug absorption: current status and unmet needs. Eur J Pharm Sci 2014; 57: 173-199
  • 52 Keller TH, Pichota A, Yin Z. A practical view of ʼdruggabilityʼ. Curr Opin Chem Biol 2006; 10: 357-361
  • 53 Kellenberger E, Hofmann A, Quinn RJ. Similar interactions of natural products with biosynthetic enzymes and therapeutic targets could explain why nature produces such a large proportion of existing drugs. Nat Prod Rep 2011; 28: 1483-1492
  • 54 Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci 2003; 43: 218-227
  • 55 Ortholand JY, Ganesan A. Natural products and combinatorial chemistry: back to the future. Curr Opin Chem Biol 2004; 8: 271-280
  • 56 Messer R, Fuhrer CA, Häner R. Natural product-like libraries based on non-aromatic, polycyclic motifs. Curr Opin Chem Biol 2005; 9: 259-265
  • 57 Camp D, Davis RA, Campitelli M, Ebdon J, Quinn RJ. Drug-like properties: guiding principles for the design of natural product libraries. J Nat Prod 2012; 75: 72-81
  • 58 Rishton GM. Reactive compounds and in vitro false positives in HTS. Drug Discov Today 1997; 2: 382-384
  • 59 Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162: 1239-1249
  • 60 Ntie-Kang F, Zofou D, Babiaka SB, Meudom R, Scharfe M, Lifongo LL, Mbah JA, Mbaze LM, Sippl W, Efange SM. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS One 2013; 8: e78085
  • 61 Ntie-Kang F, Amoa Onguéné P, Fotso GW, Andrae-Marobela K, Bezabih M, Ndom JC, Ngadjui BT, Ogundaini AO, Abegaz BM, Mevaʼa LM. Virtualizing the p-ANAPL library: a step towards drug discovery from African medicinal plants. PLoS One 2014; 9: e90655
  • 62 Buckingham J. Dictionary of natural products. London: Chapman & Hall; 1994
  • 63 Duke J. Dr. Dukeʼs phytochemical and ethnobotanical databases. ARS, USDA. Available at http://www.ars-grin.gov/duke (Oct 2009). Accessed September 26, 2013
  • 64 Nakamura Y, Afendi FM, Parvin AK, Ono N, Tanaka K, Hirai Morita A, Sato T, Sugiura T, Altaf-Ul-Amin M, Kanaya S. KNApSAcK Metabolite Activity Database for retrieving the relationships between metabolites and biological activities. Plant Cell Physiol 2014; 55: e7
  • 65 Loub WD, Farnsworth NR, Soejarto DD, Quinn ML. NAPRALERT: computer handling of natural product research data. J Chem Inf Comput Sci 1985; 25: 99-103
  • 66 Plant for a future database. Available at http://www.pfaf.org Oct 2009. Accessed September 26, 2013
  • 67 Dunkel M, Fullbeck M, Neumann S, Preissner R. SuperNatural: a searchable database of available natural compounds. Nucleic Acids Res 2006; 34: 678-683
  • 68 Chen X, Zhou H, Liu YB, Wang JF, Li H, Ung CY, Han LY, Cao ZW, Chen YZ. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol 2006; 149: 1092-1103
  • 69 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
  • 70 Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2004; 2: 3204-3218
  • 71 Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem 2014; 57: 18-28
  • 72 Medina-Franco JL. Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. J Chem Inf Model 2012; 52: 2485-2493
  • 73 Yue R, Shan L, Yang X, Zhang W. Approaches to target profiling of natural products. Curr Med Chem 2012; 19: 3841-3855
  • 74 Méndez-Lucio O, Tran J, Medina-Franco JL, Meurice N, Muller M. Towards drug repurposing in epigenetics: olsalazine as a novel hypomethylating compound active in a cellular context. ChemMedChem 2014; 9: 560-565
  • 75 Villoutreix BO, Eudes R, Miteva MA. Structure-based virtual ligand screening: recent success stories. Comb Chem High Throughput Screen 2009; 12: 1000-1016
  • 76 Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today 2011; 16: 372-376
  • 77 López-Vallejo F, Caulfield T, Martínez-Mayorga K, Giulianotti MA, Nefzi A, Houghten RA, Medina-Franco JL. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb Chem High Throughput Screen 2011; 14: 475-487
  • 78 Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK. Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 2012; 52: 867-881
  • 79 Harvey AL, Clark RL, Mackay SP, Johnston BF. Current strategies for drug discovery through natural products. Expert Opin Drug Discov 2010; 5: 559-568
  • 80 Medina-Franco JL. Advances in computational approaches for drug discovery based on natural products. Rev Latinoam Quim 2013; 41: 95-110
  • 81 Schuster D, Wolber G. Identification of bioactive natural products by pharmacophore-based virtual screening. Curr Pharm Des 2010; 16: 1666-1681
  • 82 Ehrman TM, Barlow DJ, Hylands PJ. Phytochemical informatics and virtual screening of herbs used in Chinese medicine. Curr Pharm Des 2010; 16: 1785-1798
  • 83 Shen JH, Xu XY, Cheng F, Liu H, Luo XM, Shen JK, Chen KX, Zhao WM, Shen X, Jiang HL. Virtual screening on natural products for discovering active compounds and target information. Curr Med Chem 2003; 10: 2327-2342
  • 84 Ma DL, Chan DSH, Leung CH. Molecular docking for virtual screening of natural product databases. Chem Sci 2011; 2: 1656-1665
  • 85 Geldenhuys WJ, Bishayee A, Darvesh AS, Carroll RT. Natural products of dietary origin as lead compounds in virtual screening and drug design. Curr Pharm Biotechnol 2012; 13: 117-124
  • 86 Lemmen C, Lengauer T. Computational methods for the structural alignment of molecules. J Comput Aided Mol Des 2000; 14: 215-232
  • 87 Yongye AB, Bender A, Martinez-Mayorga K. Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble. J Comput Aided Mol Des 2010; 24: 675-686
  • 88 Medina-Franco JL, Maggiora GM. Molecular similarity analysis. In: Bajorath J, editor Chemoinformatics for drug discovery. New York: John Wiley & Sons, Inc.; 2014: 343-399
  • 89 Ebalunode JO, Zheng WF. Unconventional 2D shape similarity method affords comparable enrichment as a 3D shape method in virtual screening experiments. J Chem Inf Model 2009; 49: 1313-1320
  • 90 Hu GP, Kuang GL, Xiao W, Li WH, Liu GX, Tang Y. Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening. J Chem Inf Model 2012; 52: 1103-1113
  • 91 Kalászi A, Szisz D, Imre G, Polgár T. Screen3D: a novel fully flexible high-throughput shape-similarity search method. J Chem Inf Model 2014; 54: 1036-1049
  • 92 Zhang Q, Muegge I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem 2006; 49: 1536-1548
  • 93 Mendez-Lucio O, Perez-Villanueva J, Castillo R, Medina-Franco JL. Identifying activity cliff generators of PPAR ligands using SAS maps. Mol Inf 2012; 31: 837-846
  • 94 Cruz-Monteagudo M, Medina-Franco JL, Pérez-Castillo Y, Nicolotti O, Cordeiro MNDS, Borges F. Activity cliffs in drug discovery: Dr. Jekyll or Mr. Hyde?. Drug Discov Today 2014; 19: 1069-1080
  • 95 Yongye AB, Waddell J, Medina-Franco JL. Molecular scaffold analysis of natural products databases in the public domain. Chem Biol Drug Des 2012; 80: 717-724
  • 96 Medina-Franco JL, Martinez-Mayorga K, Meurice N. Balancing novelty with confined chemical space in modern drug discovery. Expert Opin Drug Discov 2014; 9: 151-165
  • 97 López-Vallejo F, Giulianotti MA, Houghten RA, Medina-Franco JL. Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today 2012; 17: 718-726
  • 98 Bender A. How similar are those molecules after all? Use two descriptors and you will have three different answers. Expert Opin Drug Discov 2010; 5: 1141-1151
  • 99 Todeschini R, Consonni V. Molecular descriptors for chemoinformatics. 2nd. edition. New York: Wiley-VCH; 2009
  • 100 Willett P. Combination of similarity rankings using data fusion. J Chem Inf Model 2013; 53: 1-10
  • 101 Holliday JD, Kanoulas E, Malim N, Willett P. Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision. J Cheminform 2011; 3: 29
  • 102 Yongye A, Byler K, Santos R, Martínez-Mayorga K, Maggiora GM, Medina-Franco JL. Consensus models of activity landscapes with multiple chemical, conformer and property representations. J Chem Inf Model 2011; 51: 1259-1270
  • 103 Guasch L, Sala E, Castell-Auvi A, Cedo L, Liedl KR, Wolber G, Muehlbacher M, Mulero M, Pinent M, Ardevol A, Valls C, Pujadas G, Garcia-Vallve S. Identification of PPARgamma partial agonists of natural origin (I): development of a virtual screening procedure and in vitro validation. PLoS One 2012; 7: e50816
  • 104 Medina-Franco JL, Yoo J. Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases. Mol Div 2013; 17: 337-344
  • 105 Martinez-Mayorga K, Peppard TL, Ramirez-Hernandez AI, Terrazas-Alvarez DE, Medina-Franco JL. Chemoinformatics analysis and structural similarity studies of food-related databases. In: Martinez-Mayorga K, Medina-Franco JL, editors FoodInformatics: applications of chemical information to food chemistry. Heidelberg: Springer; 2014: 97-110
  • 106 Jensen K, Panagiotou G, Kouskoumvekaki I. Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level. PLoS Comput Biol 2014; 10: e1003432
  • 107 Pochetti G, Godio C, Mitro N, Caruso D, Galmozzi A, Scurati S, Loiodice F, Fracchiolla G, Tortorella P, Laghezza A, Lavecchia A, Novellino E, Mazza F, Crestani M. Insights into the mechanism of partial agonism: crystal structures of the peroxisome proliferator-activated receptor gamma ligand-binding domain in the complex with two enantiomeric ligands. J Biol Chem 2007; 282: 17314-17324
  • 108 Al-Najjar BO, Wahab HA, Tengku Muhammad TS, Shu-Chien AC, Ahmad Noruddin NA, Taha MO. Discovery of new nanomolar peroxisome proliferator-activated receptor γ activators via elaborate ligand-based modeling. Eur J Med Chem 2011; 46: 2513-2529
  • 109 Feng Y, Campitelli M, Davis RA, Quinn RJ. Chemoinformatic analysis as a tool for prioritization of trypanocidal marine derived lead compounds. Mar Drugs 2014; 12: 1169-1184
  • 110 Rosén J, Lövgren A, Kogej T, Muresan S, Gottfries J, Backlund A. ChemGPS-NP(Web): chemical space navigation online. J Comput Aided Mol Des 2009; 23: 253-259
  • 111 Rognan D. Towards the next generation of computational chemogenomics tools. Mol Inf 2013; 32: 1029-1034
  • 112 Medina-Franco JL, Aguayo-Ortiz R. Progress in the visualization and mining of chemical and target spaces. Mol Inf 2013; 32: 942-953
  • 113 Bajorath J. A perspective on computational chemogenomics. Mol Inf 2013; 32: 1025-1028
  • 114 Kjærulff SK, Wich L, Kringelum J, Jacobsen UP, Kouskoumvekaki I, Audouze K, Lund O, Brunak S, Oprea TI, Taboureau O. ChemProt-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res 2013; 4: D464-D469
  • 115 Clemons PA, Bodycombe NE, Carrinski HA, Wilson JA, Shamji AF, Wagner BK, Koehler AN, Schreiber SL. 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: 18787-18792
  • 116 Yongye AB, Medina-Franco JL. Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds. J Chem Inf Model 2012; 52: 2454-2461
  • 117 Dimova D, Hu Y, Bajorath J. Matched molecular pair analysis of small molecule microarray data identifies promiscuity cliffs and reveals molecular origins of extreme compound promiscuity. J Med Chem 2012; 55: 10220-10228
  • 118 Yongye AB, Medina-Franco JL. Toward an efficient approach to identify molecular scaffolds possessing selective or promiscuous compounds. Chem Biol Drug Des 2013; 82: 367-375
  • 119 Dossetter AG, Griffen EJ, Leach AG. Matched molecular pair analysis in drug discovery. Drug Discov Today 2013; 18: 724-731
  • 120 http://pdb.rcsb.org/pdb/static.do?p=general_information/pdb_statistics/index.html Accessed June 22, 2014
  • 121 Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982; 161: 269-288
  • 122 Lengauer T, Rarey M. Computational methods for biomolecular docking. Curr Opin Struct Biol 1996; 6: 402-406
  • 123 Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 1996; 9: 1-5
  • 124 Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261: 470-489
  • 125 Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004; 47: 1739-1749
  • 126 Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267: 727-748
  • 127 Jain AN. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 2003; 46: 499-511
  • 128 Li Y, Han L, Liu Z, Wang R. Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 2014; 54: 1717-1736
  • 129 Bissantz C, Folkers G, Rognan D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 2000; 43: 4759-4767
  • 130 Bar-Haim S, Aharon A, Ben-Moshe T, Marantz Y, Senderowitz H. SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization. J Chem Inf Model 2009; 49: 623-633
  • 131 Teramoto R, Fukunishi H. Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors. J Chem Inf Model 2008; 48: 747-754
  • 132 Clark RD, Strizhev A, Leonard JM, Blake JF, Matthew JB. Consensus scoring for ligand/protein interactions. J Mol Graph Model 2002; 20: 281-295
  • 133 Charifson PS, Corkery JJ, Murcko MA, Walters WP. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 1999; 42: 5100-5109
  • 134 Feher M. Consensus scoring for protein-ligand interactions. Drug Discov Today 2006; 11: 421-428
  • 135 Plewczynski D, Łaźniewski M, von Grotthuss M, Rychlewski L, Ginalski K. VoteDock: consensus docking method for prediction of protein-ligand interactions. J Comput Chem 2011; 32: 568-581
  • 136 Houston DR, Walkinshaw MD. Consensus docking: improving the reliability of docking in a virtual screening context. J Chem Inf Model 2013; 53: 384-390
  • 137 Paul N, Rognan D. ConsDock: A new program for the consensus analysis of protein-ligand interactions. Proteins 2002; 47: 521-533
  • 138 Scior T, Verhoff M, Gutierrez-Aztatzi I, Ammon HP, Laufer S, Werz O. Interference of boswellic acids with the ligand binding domain of the glucocorticoid receptor. J Chem Inf Model 2014; 54: 978-986
  • 139 Peeters M, Li Q, Elands R, van Westen GJ, Lenselink EB, Müller CE, IJzerman AP. Domains for activation and inactivation in G protein-coupled receptors–a mutational analysis of constitutive activity of the adenosine A2B receptor. Biochem Pharmacol 2014; 92: 348-357
  • 140 Thiyagarajan V, Lin SH, Chia YC, Weng CF. A novel inhibitor, 16-hydroxy-cleroda-3,13-dien-16,15-olide, blocks the autophosphorylation site of focal adhesion kinase (Y397) by molecular docking. Biochim Biophys Acta 2013; 1830: 4091-4101
  • 141 Hussain A, Melville JL, Hirst JD. Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin. J Comput Aided Mol Des 2010; 24: 1-15
  • 142 Koukoulitsa C, Zervou M, Demetzos C, Mavromoustakos T. Comparative docking studies of labdane-type diterpenes with forskolin at the active site of adenylyl cyclase. Bioorg Med Chem 2008; 16: 8237-8243
  • 143 http://ec.europa.eu/research/health/infectious-diseases/antimicrobial-drug-resistance/projectsfp7_en.html Accessed June 22, 2014
  • 144 http://www.nabativi.org/ Accessed September 26, 2013
  • 145 Srinivas N, Jetter P, Ueberbacher BJ, Werneburg M, Zerbe K, Steinmann J, Van der Meijden B, Bernardini F, Lederer A, Dias RL, Misson PE, Henze H, Zumbrunn J, Gombert FO, Obrecht D, Hunziker P, Schauer S, Ziegler U, Käch A, Eberl L, Riedel K, DeMarco SJ, Robinson JA. Peptidomimetic antibiotics target outer-membrane biogenesis in Pseudomonas aeruginosa . Science 2010; 327: 1010-1013
  • 146 Chan FY, Sun N, Neves MA, Lam PC, Chung WH, Wong LK, Chow HY, Ma DL, Chan PH, Leung YC, Chan TH, Abagyan R, Wong KY. Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. J Chem Inf Model 2013; 53: 2131-2140
  • 147 Huang KJ, Lin SH, Lin MR, Ku H, Szkaradek N, Marona H, Hsu A, Shiuan D. Xanthone derivatives could be potential antibiotics: virtual screening for the inhibitors of enzyme I of bacterial phosphoenolpyruvate-dependent phosphotransferase system. J Antibiot (Tokyo) 2013; 66: 453-458
  • 148 Harris SM, McFeeters H, Ogungbe IV, Cruz-Vera LR, Setzer WN, Jackes BR, McFeeters RL. Peptidyl-tRNA hydrolase screening combined with molecular docking reveals the antibiotic potential of Syzygium johnsonii bark extract. Nat Prod Commun 2011; 6: 1421-1424
  • 149 Kirchmair J, Distinto S, Liedl KR, Markt P, Rollinger JM, Schuster D, Spitzer GM, Wolber G. Development of anti-viral agents using molecular modeling and virtual screening techniques. Infect Disord Drug Targets 2011; 11: 64-93
  • 150 Srinivasan S, Sarada DV. Antifungal activity of phenyl derivative of pyranocoumarin from Psoralea corylifolia L. seeds by inhibition of acetylation activity of trichothecene 3-o-acetyltransferase (Tri101). J Biomed Biotechnol 2012; 2012: e310850
  • 151 Singh C, Atri N. Chemo-informatic design of antibiotic geldenamycin analogs to target stress proteins HSP90 of pathogenic protozoan parasites. Bioinformation 2013; 9: 329-333
  • 152 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: 217-226
  • 153 Vigers GP, Rizzi JP. Multiple active site corrections for docking and virtual screening. J Med Chem 2004; 47: 80-89
  • 154 Paul N, Kellenberger E, Bret G, Müller P, Rognan D. Recovering the true targets of specific ligands by virtual screening of the protein data bank. Proteins 2004; 54: 671-680
  • 155 Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-242
  • 156 Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res 2004; 32: D431-D433
  • 157 Chen X, Ji ZL, Chen YZ. TTD: therapeutic target database. Nucleic Acids Res 2002; 30: 412-415
  • 158 Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H. PDTD: a web-accessible protein database for drug target identification. BMC Bioinf 2008; 9: e104
  • 159 Kellenberger E, Muller P, Schalon C, Bret G, Foata N, Rognan D. sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank. J Chem Inf Model 2006; 46: 717-727
  • 160 Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2008; 36: 901-906
  • 161 Do QT, Bernard P. Pharmacognosy and reverse pharmacognosy: a new concept for accelerating natural drug discovery. IDrugs 2004; 7: 1017-1027
  • 162 Do QT, Renimel I, Andre P, Lugnier C, Muller CD, Bernard P. Reverse pharmacognosy: application of selnergy, a new tool for lead discovery. The example of epsilon-viniferin. Curr Drug Discov Technol 2005; 2: 161-167
  • 163 Do QT, Lamy C, Renimel I, Sauvan N, André P, Himbert F, Morin-Allory L, Bernard P. Reverse pharmacognosy: identifying biological properties for plants by means of their molecule constituents: application to meranzin. Planta Med 2007; 73: 1235-1240
  • 164 Rollinger JM. Accessing target information by virtual parallel screening–the impact on natural product research. Phytochem Lett 2009; 2: 53-58
  • 165 Chen SJ. A potential target of Tanshinone IIA for acute promyelocytic leukemia revealed by inverse docking and drug repurposing. Asian Pac J Cancer Prev 2014; 15: 4301-4305
  • 166 Chen YZ, Ung CY. Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. J Mol Graph Model 2001; 20: 199-218
  • 167 Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 2014; 54: 1676-1686
  • 168 Grinter SZ, Liang Y, Huang SY, Hyder SM, Zou X. An inverse docking approach for identifying new potential anti-cancer targets. J Mol Graph Model 2011; 29: 795-799
  • 169 Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H. TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 2006; 34: W219-W224
  • 170 Hui-fang L, Qing S, Jian Z, Wei F. Evaluation of various inverse docking schemes in multiple targets identification. J Mol Graph Model 2010; 29: 326-330
  • 171 Nicola G, Liu T, Gilson MK. Public domain databases for medicinal chemistry. J Med Chem 2012; 55: 6987-7002
  • 172 Wale N, Karypis G. Target fishing for chemical compounds using target-ligand activity data and ranking based methods. J Chem Inf Model 2009; 49: 2190-2201
  • 173 Pandini A, Fraccalvieri D, Bonati L. Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry. Curr Top Med Chem 2013; 13: 642-651
  • 174 Rognan D. Structure-based approaches to target fishing and ligand profiling. Mol Inf 2010; 29: 176-187
  • 175 Wermuth G, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 1998; 70: 1129-1143
  • 176 Nettles JH, Jenkins JL, Bender A, Deng Z, Davies JW, Glick M. Bridging chemical and biological space: “target fishing” using 2D and 3D molecular descriptors. J Med Chem 2006; 49: 6802-6810
  • 177 Todeschini R, Lasagni M, Marengo E. New molecular descriptors for 2D and 3D structures. Theory. J Chemometr 1994; 8: 263-272
  • 178 Bravi G, Gancia E, Mascagni P, Pegna M, Todeschini R, Zaliani A. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D QSAR study in a series of steroids. J Comput Aided Mol Des 1997; 11: 79-92
  • 179 Medina-Franco JL, Martínez-Mayorga K, Bender A, Marín RM, Giulianotti MA, Pinilla C, Houghten RA. Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 2009; 49: 477-491
  • 180 Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 – a public library of challenging docking benchmark sets. J Chem Inf Model 2013; 53: 1447-1462
  • 181 Bender A, Jenkins JL, Scheiber J, Sukuru SCK, Glick M, Davies JW. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model 2009; 49: 108-119
  • 182 Scior T, Bernard P, Medina-Franco JL, Maggiora GM. Large compound databases for structure-activity relationships studies in drug discovery. Mini Rev Med Chem 2007; 7: 851-860