Virtual screening for the discovery of bioactive natural products

  • Judith M. Rollinger
  • Hermann Stuppner
  • Thierry Langer
Part of the Progress in Drug Research book series (PDR, volume 65)


In this survey the impact of the virtual screening concept is discussed in the field of drug discovery from nature. Confronted by a steadily increasing number of secondary metabolites and a growing number of molecular targets relevant in the therapy of human disorders, the huge amount of information needs to be handled. Virtual screening filtering experiments already showed great promise for dealing with large libraries of potential bioactive molecules. It can be utilized for browsing databases for molecules fitting either an established pharmacophore model or a three dimensional (3D) structure of a macromolecular target. However, for the discovery of natural lead candidates the application of this in silico tool has so far almost been neglected. There are several reasons for that. One concerns the scarce availability of natural product (NP) 3D databases in contrast to synthetic libraries; another reason is the problematic compatibility of NPs with modern robotized high throughput screening (HTS) technologies. Further arguments deal with the incalculable availability of pure natural compounds and their often too complex chemistry. Thus research in this field is time-consuming, highly complex, expensive and ineffective. Nevertheless, naturally derived compounds are among the most favorable source of drug candidates. A more rational and economic search for new lead structures from nature must therefore be a priority in order to overcome these problems.

Here we demonstrate some basic principles, requirements and limitations of virtual screening strategies and support their applicability in NP research with already performed studies. A sensible exploitation of the molecular diversity of secondary metabolites however asks for virtual screening concepts that are interfaced with well-established strategies from classical pharmacognosy that are used in an effort to maximize their efficacy in drug discovery. Such integrated virtual screening workflows are outlined here and shall help to motivate NP researchers to dare a step towards this powerful in silico tool.


Virtual Screening Pharmacophore Model Bioactive Natural Product Natural Product Research Virtual Screen 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brown D, Superti-Furga G (2003) Rediscovering the sweet spot in drug discovery. Drug Discov Today 8: 1067–1077PubMedCrossRefGoogle Scholar
  2. 2.
    Drews J (2003) Strategic trends in the drug industry. Drug Discov Today 8: 411–420PubMedCrossRefGoogle Scholar
  3. 3.
    Smith A (2002) Screening for drug discovery: the leading question. Nature 418: 453–459PubMedGoogle Scholar
  4. 4.
    Strohl WR (2000) The role of natural products in a modern drug discovery program. Drug Disc Today 5: 39CrossRefGoogle Scholar
  5. 5.
    Newman DJ, Cragg GM (2007) Natural products as sources of new drugs over the last 25 years. J Nat Prod 70: 461–477CrossRefGoogle Scholar
  6. 6.
    Harvey A (2000) Strategies for discovering drugs from previously unexplored natural products. Drug Disc Today 5: 294–300CrossRefGoogle Scholar
  7. 7.
    Clardy J, Walsh C (2004) Lessons from natural molecules. Nature 432: 829–837PubMedCrossRefGoogle Scholar
  8. 8.
    Jones WP, Chin YW, Kinghorn AD (2006) The role of pharmacognosy in modern medicine and pharmacy. CurrDrug Targets 7: 247–264CrossRefGoogle Scholar
  9. 9.
    Dictionary of Natural Products provided by Chapman &Hall/CRC. Available at: (accessed in January 2007)Google Scholar
  10. 10.
    Tulp M, Bohlin L (2005) Rediscovery of known natural compounds: Nuisance or goldmine? BioorgMed Chem 13: 5274–5282CrossRefGoogle Scholar
  11. 11.
    Sitte P, Weiler EW, Kadereit JW, Bresinsky A, Körner C (2002) Strasburger — Lehrbuch der Botanik, 35. Auflage, Spektrum Akademischer Verlag, Heidelberg, Berlin, 339–351Google Scholar
  12. 12.
    Balandrin M (1993) Plant-derived natural products in drug discovery and development: an overview. Amer Chem Soc Symposium Series No 534: 2–12Google Scholar
  13. 13.
    Cragg GM, Newman DJ, Snader KM (1997) Natural products in drug discovery and development. J Nat Prod 60: 52–60PubMedCrossRefGoogle Scholar
  14. 14.
    Henkel T, Brunne RM, Muller H, Reichel F (1999) Statistical investigation into the structural complementarity of natural products and synthetic compounds. Angew Chem Int Ed 38: 643–647CrossRefGoogle Scholar
  15. 15.
    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar M, Doyle M, FitzHugh W et al (2001) Initial sequencing and analysis of the human genome. Nature 409: 860–921PubMedCrossRefGoogle Scholar
  16. 16.
    Deisenhofer J, Smith JL (2001) Proteins. Curr Opin Struc Biol 11: 701–702Google Scholar
  17. 17.
    Maggio ET, Ramnarayan K (2001) Recent developments in computational proteomics. Drug Disc Today 6: 996–1004CrossRefGoogle Scholar
  18. 18.
    Potterat O, Hamburger M (2006) Natural products in drug discovery — concepts and approaches for tracking bioactivity. Curr Org Chem 10: 899–920CrossRefGoogle Scholar
  19. 19.
    Pottereat O (2006) Targeted approaches in natural product lead discovery. Chimia 60: 19–22CrossRefGoogle Scholar
  20. 20.
    Morrell J (1996) Mining information from databases for drug discovery. Book Abstr 211th ACS National Meeting CINF-052Google Scholar
  21. 21.
    Gasteiger J, Teckentrup A, Terfloth L, Spycher S (2003) Neural networks as data mining tools in drug design. J Phys Org Chem 16: 232–245CrossRefGoogle Scholar
  22. 22.
    Xu J, Hagler A (2002) Chemoinformatics and drug discovery. Molecules 7: 566–600CrossRefGoogle Scholar
  23. 23.
    Alvarez AJ, Shoichet B (2005) Virtual screening in drug discovery. Taylor & Francis, CRC-Press, Boca RatonGoogle Scholar
  24. 24.
    Langer T, Hoffmann RD (2006) Pharmacophores and pharmacophore searches. In: Methods and principles in medicinal chemistry Vol. 32, Wiley-VCH, WeinheimGoogle Scholar
  25. 25.
    Manly CJ, Louise-May S, Hammer JD (2001) The impact of informatics and computational chemistry on synthesis and screening. Drug Discov Today 6: 1101–1110PubMedCrossRefGoogle Scholar
  26. 26.
    Langer T, Hoffmann RD (2001) Virtual screening: an effective tool for lead structure discovery? CurrPharm Des 7: 509–527Google Scholar
  27. 27.
    Böhm HJ, Schneider G (2000) Virtual screening for bioactive molecules. Wiley, New YorkGoogle Scholar
  28. 28.
    Krovat EM, Steindl T, Langer T (2005) Recent advances in docking and scoring. Curr Comput-Aided Drug Des 1: 93–102CrossRefGoogle Scholar
  29. 29.
    Abagyan R, Totrov M (2001) High-throughput docking for lead generation. Curr Opin Chem Biol 5: 375–382PubMedCrossRefGoogle Scholar
  30. 30.
    Schneider G, Böhm HJ (2002) Virtual screening and fast automated docking methods. Drug Disc Today 7: 64–70Google Scholar
  31. 31.
    Shoichet BK, McGovern SL, Wei B, Irwin JJ (2002) Lead discovery using molecular docking. Curr Opin Chem Biol 6: 439–446PubMedCrossRefGoogle Scholar
  32. 32.
    Stahl M, Rarey M (2001) Detailed analysis of scoring functions for virtual screening. J Med Chem 44: 1035–1042PubMedCrossRefGoogle Scholar
  33. 33.
    Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42: 5100–5109PubMedCrossRefGoogle Scholar
  34. 34.
    Wang R, Wang S (2001) How does consensus scoring work for virtual library screening? An idealized computer experiment. J Chem Inf Comput Sci 41: 1422–1426PubMedCrossRefGoogle Scholar
  35. 35.
    Liu B, Zhou J (2005) SARS-CoV protease inhibitors design using virtual screening method from natural products libraries. J Comp Chem 26: 484–490CrossRefGoogle Scholar
  36. 36.
    Toney JH, Navas-Martin S, Weiss SR, Koeller A (2004) Sabadinine: a potential non-peptide anti-severe acute-respiratory-syndrome agent identified using structure-aided design. J Med Chem 47: 1079–1080PubMedCrossRefGoogle Scholar
  37. 37.
    Cozza G, Bonvini P, Zorzi E, Poletto G, Pagano MA, Sarno S, Donella-Deana A, Zagotto G, Rosolen A, Pinna LA et al (2006) Identification of ellagic acid as potent inhibitor of protein kinase CK2: a successful example of a virtual screening application. J Med Chem 49: 2363–2366PubMedCrossRefGoogle Scholar
  38. 38.
    Zhao L, Brinton RD (2005) Structure-based virtual screening for plant-based ERß-selective ligands as potential preventative therapy against age-related neuro-degenerative diseases. J Med Chem 48: 3463–3466PubMedCrossRefGoogle Scholar
  39. 39.
    Liu H, Li Y, Song M, Tan X, Cheng F, Zheng S, Shen J, Luo X, Ji R, Yue J et al (2003) Structure-based discovery of potassium channel blockers from natural products virtual screening and electrophysiological assay testing. Chem Biol 10: 1103–1113PubMedCrossRefGoogle Scholar
  40. 40.
    Langer T, Hoffmann RD (2006) Pharmacophore modelling: applications in drug discovery. Exp Opin Drug Discov 1: 261–267CrossRefGoogle Scholar
  41. 41.
    Doman TN, McGovern SL, Witherbee BJ, Kasten TP, Kurumbail R, Stallings WC, Connolly DT, Shoichet BK (2002) Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 45: 2213–2221PubMedCrossRefGoogle Scholar
  42. 42.
    Samiulla DS, Vaidyanathan VV, Arun PC, Balan G, Blaze M, Bondre S, Chandrasekhar G, Gadakh A, Kumar R, Kharvi G et al (2005) Rational selection of structurally diverse natural product scaffolds with favorable ADME properties for drug discovery. Mol Divers 9: 131–139PubMedCrossRefGoogle Scholar
  43. 43.
    Testa B (1984) Drugs? Drug research? Advances in drug research? Musings of a medicinal chemist. Adv Drug Res 13: 1–58Google Scholar
  44. 44.
    Smith DA, van de Waterbeemd H, Walker DK (2006) Pharmacokinetics and metabolism in drug design. 2nd Ed., Wiley-VCH, WeinheimGoogle Scholar
  45. 45.
    Avdeef A, Testa B (2002) Physicochemical profiling in drug research: a brief survey of the state-of-the-art of experimental techniques. Cell Mol Life Sci 59: 1681–1689PubMedCrossRefGoogle Scholar
  46. 46.
    Feher M, Schmidt JM (2003) Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci 43: 218–227PubMedCrossRefGoogle Scholar
  47. 47.
    Stahura F, Godden JW, Ling X, Bajorath J (2002) Distinguishing between natural products and synthetic molecules by descriptor Shannon entropy analysis and binary QSAR calculations. J Chem Inf Comput Sci 40: 1254–1252Google Scholar
  48. 48.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23: 3–25CrossRefGoogle Scholar
  49. 49.
    Congreve M, Murray CW, Blundell TL (2005) Structural biology and drug discovery. Drug Disc Today 10: 895–907CrossRefGoogle Scholar
  50. 50.
    Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P (2000) The protein data bank. Nucleic Acids Res 28: 235–242PubMedCrossRefGoogle Scholar
  51. 51.
    Wolber G, Langer T (2000) LigandScout: 3D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45: 160–169CrossRefGoogle Scholar
  52. 52.
    Krovat EM, Frühwirth KH, Langer T (2005) Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa. J Chem Inf Model 45: 146–159PubMedCrossRefGoogle Scholar
  53. 53.
    Rella M, Rushworth C, Guy JL, Turner AJ, Langer T, Jackson RM (2006) Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. J Chem Inf Model 46: 708–716PubMedCrossRefGoogle Scholar
  54. 54.
    Barreca ML, De Luca L, Iraci N, Rao A, Ferro S, Maga G, Chimirri A (2007) Structure-based pharmacophore identification of new chemical scaffolds as non-nucleoside reverse transcriptase inhibitors. J Chem Inf Model 47: 557–562PubMedCrossRefGoogle Scholar
  55. 55.
    Schuster D, Laggner C, Steindl TM, Langer T (2006) Development and validation of an in silico P450 profiler based on pharmacophore models. Curr Drug Discov Technol 3: 1–48PubMedCrossRefGoogle Scholar
  56. 56.
    Steindl TM, Schuster D, Laggner C, Langer T (2006) Parallel screening: a novel concept in pharmacophore modelling and virtual screening. J Chem Inf Model 46: 2146–2157PubMedCrossRefGoogle Scholar
  57. 57.
    Steindl TM, Schuster D, Wolber G, Laggner C, Langer T (2007) High throughput structure-based pharmacophore modeling as a basis for successful parallel virtual screening. J Comput-aided Mol Des ASAP doi 10.1007/s10822-006-9066-yGoogle Scholar
  58. 58.
    Steindl TM, Schuster D, Laggner C, Chuang K, Hoffmann R, Langer T (2007) Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models. J Chem Inf Model 47: 563–571PubMedCrossRefGoogle Scholar
  59. 59.
    Nikolovska-Coleska Z, Xu L, Hu Z, Tomita Y, Li P, Roller PP, Wang R, Fang X, Guo R, Zhang M et al (2004) Discovery of embelin as a cell-permeable, small-molecular weight inhibitor of XIAP through structure-based computational screening of a traditional herbal medicine three-dimensional structure database. J Med Chem 47: 2430–2440PubMedCrossRefGoogle Scholar
  60. 60.
    Wu G, Chai J, Suber TL, Wu JW, Du C, Wang X, Shi Y (2000) Structural basis of IAP recognition by Smac/DIABLO. Nature 408: 1008–1012PubMedCrossRefGoogle Scholar
  61. 61.
    Rollinger JM, Hornick A, Langer T, Stuppner H, Prast H (2004) Acetylcholinesterase inhibitory activity of scopolin and scopoletin discovered by virtual screening of natural products. J Med Chem 47: 6248–6254PubMedCrossRefGoogle Scholar
  62. 62.
    Langer T, Krovat EM (2003) Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr Opin Drug Discov Dev 6: 370–376Google Scholar
  63. 63.
    Schuster D, Maurer E, Laggner C, Nashev L, Wilckens T, Langer T, Odermatt A (2006) The discovery of new 11gb-hydroxysteroid dehydrogenase Type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J Med Chem 49: 3454–3466PubMedCrossRefGoogle Scholar
  64. 64.
    Schuster D, Laggner C, Steindl TM, Palusczak A, Hartmann RW, Langer T (2006) Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors. J Chem Inf Model 46: 1301–1311PubMedCrossRefGoogle Scholar
  65. 65.
    Kurogi Y, Güner OF (2001) Pharmacophore modeling and three-dimensional database searching for drug design using Catalyst. Curr Med Chem 8: 1035–1055PubMedGoogle Scholar
  66. 66.
    Güner O, Clement O, Kurogi Y (2004) Pharmacophore modeling and three dimensional database searching for drug design using CATALYST: Recent advances. Curr Med Chem 11: 763–771Google Scholar
  67. 67.
    Füllbeck M, Huang X, Dumdey R, Frommel C, Dubiel W, Preissner R (2005) Novel curcumin-and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells. BMC Cancer 5: 97PubMedCrossRefGoogle Scholar
  68. 68.
    Laggner C, Schieferer C, Fiechtner B, Poles G, Hoffmann RD, Glossmann H, Langer T, Moebius F (2005) Feature based pharmacophore models for sigma1 receptor, ERG2 and EBP. J Med Chem 48: 4754–4764PubMedCrossRefGoogle Scholar
  69. 69.
    Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design, 2nd Ed. Wiley-VCH, WeinheimGoogle Scholar
  70. 70.
    Wagner S, Hofmann A, Siedle B, Terfloth L, Merfort I, Gasteiger J (2006) Development of a structural model for NF-κB inhibition of sesquiterpene lactones using self-organizing neural networks. J Med Chem 49: 2241–2252PubMedCrossRefGoogle Scholar
  71. 71.
    Sangma C, Chuakheaw D, Jongkon N, Saenbandit K, Nunrium P, Uthayopas P, Hannongbua S (2005) Virtual screening for anti-HIV-1 RT and anti-HIV-1 PR inhibitors from the Thai Medicinal Plants Database: A combined docking with neural networks approach. Comb Chem HTS 8: 417–429Google Scholar
  72. 72.
    Cherkasov A, Shi Z, Fallahi M, Hammond GL (2005) Successful in silico discovery of novel nonsteroidal ligands for human sex hormone binding globulin. J Med Chem 48: 3203–3213PubMedCrossRefGoogle Scholar
  73. 73.
    Svetnik V, Liaw A, Tong D, Culberson C, Sheridan RP, Feuston BP (2003) Random Forest: a classification and regression tool for compound classification and QSAR Modeling. J Chem Inf Comput Sci 43: 1947–1958PubMedCrossRefGoogle Scholar
  74. 74.
    Ehrman TM, Barlow DJ, Hylands PJ (2007) Virtual screening of Chinese herbs with random forest. J Chem Inf Model ASAP 10.1021/ci600289vGoogle Scholar
  75. 75.
    Kirchmair J, Laggner C, Wolber G, Langer T (2005) Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model 45: 422–430PubMedCrossRefGoogle Scholar
  76. 76.
    Poroikov VV, Filimonov DM, Ihlenfeldt WD, Gloriozova TA, Lagunin AA, Borodina YV, Stepanchikova AV, Nicklaus MC (2003) PASS Biological activity predictions in the enhanced open NCI database browser. J Chem Inf Comput Sci 43: 228–236PubMedCrossRefGoogle Scholar
  77. 77.
    Lu A, Liu B, Liu H, Zhou J, Xie G (2004) A traditional Chinese medicine plant-compound database aid its application for searching. Int Electron J Mol Des 3: 672–683Google Scholar
  78. 78.
    Füllbeck M, Michalsky E, Dunkel M, Preissner R (2006) Natural products: sources and databases. Nat Prod Rep 23: 347–356PubMedCrossRefGoogle Scholar
  79. 79.
    Dunkel M, Füllbeck M, Neumann S, Preissner R (2006) SuperNatural: a searchable database of available natural compounds. Nucleic Acid Res 34: D678–683PubMedCrossRefGoogle Scholar
  80. 80.
    Lei J, Zhou J (2002) A marine natural product database. J Chem Inf Comp Sci 42: 742–748CrossRefGoogle Scholar
  81. 81.
    Rollinger JM, Haupt S, Stuppner H, Langer T (2004) Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example. J Chem Inf Comp Sci 44: 480–488CrossRefGoogle Scholar
  82. 82.
    Bernard P, Berton JY, Chrétien JR (1999) Computer-aided molecular selection and design of natural bioactive molecules. Curr Opin Drug Disc Dev 2: 213–223Google Scholar
  83. 83.
    Ehrman TM, Barlow DJ, Hylands PJ (2007) Phytochemical databases of Chinese herbal constituents and bioactive plant compounds with known target specifities. J Chem Inf Model ASAP 10.1021/ci600288mGoogle Scholar
  84. 84.
    Rollinger JM, Langer T, Stuppner H (2006) Strategies for efficient lead structure discovery from natural products. Curr Med Chem 13: 1491–1507PubMedCrossRefGoogle Scholar
  85. 85.
    Rollinger JM, Langer T, Stuppner H (2006) Integrated in silico tools to exploit the natural products’ bioactivity. Planta Med 72: 671–678PubMedCrossRefGoogle Scholar
  86. 86.
    Van de Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nature Rev Drug Disc 2: 192–204CrossRefGoogle Scholar
  87. 87.
    Rollinger JM, Bodensieck A, Seger C, Ellmerer EP, Bauer R, Langer T, Stuppner H (2005) Discovering COX-inhibiting constituents of Morus root bark: Activity-guided versus computer-aided methods. Planta Med 71: 399–405PubMedCrossRefGoogle Scholar
  88. 88.
    Bernard P, Scior T, Didier B, Hibert M, Berthon JY (2001) Ethnopharmacology and bioinformatic combination for leads discovery: application to phospholipase A2 inhibitors. Phytochemistry 58: 865–874PubMedCrossRefGoogle Scholar
  89. 89.
    Van de Waterbeemd H (2005) Which in vitro screens guide the prediction of oral absorption and volume of distribution? Bas Clin Pharmacol Toxicol 96: 162–166CrossRefGoogle Scholar
  90. 90.
    Rollinger JM, Mock P, Zidorn C, Ellmerer EP, Langer T, Stuppner H (2005) Application of the in combo screening approach for the discovery of non-alkaloid acetylcholinesterase inhibitors from Cichorium intybus. Curr Drug Discov Techn 2:185–193; Erratum (2006) 3: 89CrossRefGoogle Scholar
  91. 91.
    Bajorath J (2002) Virtual screening in drug discovery: methods, expectations and reality. Curr Drug Disc March: 24–28Google Scholar
  92. 92.
    Bajorath J (2002) Integration of virtual and high-throughput screening. Nature Rev Drug Discovery 1 Nov: 882–894Google Scholar
  93. 93.
    Chimenti F, Cottiglia F, Bonsignore L, Casu L, Casu M, Floris C, Secci D, Bolasco A, Cimenti P, Granese A et al (2006) Quercetin as the active principle of Hypericum hircinum exerts a selective inhibitory activity against MAO-A: extraction, biological analysis, and computational study. J Nat Prod 69: 945–949PubMedCrossRefGoogle Scholar
  94. 94.
    Rollinger JM, Schuster D, Baier E, Ellmerer EP, Langer T, Stuppner H (2006) Taspine: Bioactivity-guided isolation and molecular ligand-target insight of a potent acetylcholinesterase inhibitor from Magnolia x soulangiana. J Nat Prod 69: 1341–1346PubMedCrossRefGoogle Scholar
  95. 95.
    Rognan D (2006) In silico screening of the protein structure repertoire and of protein families. Chemogenomics 109–131Google Scholar
  96. 96.
    Paul N, Kellenberger E, Bret G, Mueller P, Rognan D (2004) Recovering the true targets of specific ligands by virtual screening of the Protein Data Bank. Proteins 54: 671–680PubMedCrossRefGoogle Scholar
  97. 97.
    Nettles JH, Jenkins JL, Bender A, Deng Z, Davies JW, Glick M (2006) Bridging chemical and biological space: “target fishing” using 2D and 3D molecular descriptors. J Med Chem 49: 6802–6810PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag, Basel (Switzerland) 2008

Authors and Affiliations

  • Judith M. Rollinger
    • 1
  • Hermann Stuppner
    • 1
    • 2
  • Thierry Langer
    • 2
    • 3
  1. 1.Institute of Pharmacy/PharmacognosyLeopold-Franzens University of InnsbruckInnsbruckAustria
  2. 2.Software Engineering and ConsultingInte:Ligand GmbHMaria EnzersdorfAustria
  3. 3.Institute of Pharmacy/Pharmaceutical Chemistry/Computer Aided Molecular Design GroupLeopold-Franzens University of InnsbruckInnsbruckAustria

Personalised recommendations