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Drug Metabolism and Personalized Therapy

Official journal of the European Society of Pharmacogenomics and Personalised Therapy

Editor-in-Chief: Llerena, Adrián

Editorial Board: Benjeddou, Mongi / Chen, Bing / Hubacek, Jaroslav A. / Ingelman-Sundberg, Magnus / Manolopoulos, Vangelis G. / Marc, Janja / Meyer, Urs A. / Nair, Sujit / Nofziger, Charity / Peiro, Ana / Simmaco, Maurizio / Schaik, Ron / Shin, Jae-Gook / Visvikis-Siest, Sophie

CiteScore 2018: 1.01

SCImago Journal Rank (SJR) 2018: 0.277
Source Normalized Impact per Paper (SNIP) 2018: 0.446

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Volume 27, Issue 4


In silico pharmacology for a multidisciplinary drug discovery process

Santiago Schiaffino Ortega
  • Departamento de Química Farmacéutica y Orgánica, Facultad de Farmacia, Universidad de Granada, Granada, España
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Luisa Carlota López Cara
  • Departamento de Química Farmacéutica y Orgánica, Facultad de Farmacia, Universidad de Granada, Granada, España
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ María Kimatrai Salvador
  • Corresponding author
  • Departamento de Química Farmacéutica y Orgánica, Facultad de Farmacia, Universidad de Granada, Granada, España
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2012-11-09 | DOI: https://doi.org/10.1515/dmdi-2012-0021


The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8–15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.

Keywords: docking; in silico pharmacology; pharmacophore; rational drug design; structure-activity relationship (SAR)


  • 1.

    Verbanac D. Predictive methods as a powerful tool in drug discovery. Biochem Med 2010;20:314–8.CrossrefGoogle Scholar

  • 2.

    Khan F, Yadav DK, Maurya A, Srivastava SK. Modern methods and web resources in drug design and discovery. Lett Drug Design Discov 2011;8:469–90.CrossrefGoogle Scholar

  • 3.

    Dimasi JA, Hansen RW, Grabowski HA, Lasagna L. Research and development cost for new drugs by therapeutic category. A study of the United States pharmaceutical industry. Pharmacoeconomics 1995;7:152–69.CrossrefPubMedGoogle Scholar

  • 4.

    Gohlke H, Klebe G. Approaches to the description and prediction of the binding affinity small-molecule ligands to macromolecular receptors. Angew Chem Int Ed Engl 2002;41:2644–76.CrossrefPubMedGoogle Scholar

  • 5.

    Searls DB. Using bioinformatics in gene and drug discovery. Drug Discov Today 2000;5:135–43.PubMedCrossrefGoogle Scholar

  • 6.

    Lenz GR, Nash HM, Jindal S. Chemical ligands, genomics and drug discovery. Drug Discov Today 2000;5:145–56.CrossrefPubMedGoogle Scholar

  • 7.

    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.CrossrefPubMedGoogle Scholar

  • 8.

    Dean PM. Molecular recognition: the measurement and search for molecular similarity in ligand-receptor interaction. In: Maggiora GM, Johnson MA, editors. Concepts and applications of molecular similarity. New York: John Wiley & Sons, 1990:99–117.Google Scholar

  • 9.

    Willett P. Similarity-based approaches to virtual screening. Biochem Soc Trans 2003;31:603–6.PubMedGoogle Scholar

  • 10.

    Hansch C, Fujita T. Rho-Sigma-Pi analysis. Method for correlation of biological activity–chemical structure. J Am Chem Soc 1964;86:1616–26.CrossrefGoogle Scholar

  • 11.

    Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry. Pure Appl Chem 1998;70:1129–43.CrossrefGoogle Scholar

  • 12.

    Wang SM, Zaharevitz DW, Sharma R, Marquez VE, Lewin NE, Du L, et al. The discovery of novel, structurally diverse protein kinase C agonist through computer 3D-database pharmacophore search. Molecular modeling studies. J Med Chem 1994;37:4479–89.CrossrefGoogle Scholar

  • 13.

    Carlson HA, Masukawa KM, Rubins K, Bushman FD, Jorgensen WL, Lins RD, et al. Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 2000;43:2100–14.PubMedGoogle Scholar

  • 14.

    Nicklaus MC, Neamati N, Hong HX, Mazumder A, Sunder S, Chen J, et al. HIV-1 integrase pharmacophore: discovery of inhibitors through three-dimensional database searching. J Med Chem 1997;40:920–9.PubMedGoogle Scholar

  • 15.

    Ekins S, Swaan PW. Development of computational models for enzymes, transporters, channels and receptors relevant to ADME/Tox. Rev Comp Chem 2004;20:333–415.Google Scholar

  • 16.

    Rognan D. Chemogenomic approaches to rational drug design. Br J Pharmacol 2007;152:38–52.CrossrefPubMedGoogle Scholar

  • 17.

    Klabunde T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br J Pharmacol 2007;152:5–7.PubMedCrossrefGoogle Scholar

  • 18.

    Leach AR, Gillet VJ, Lewis RA, Taylor R. Three-dimensional pharmacophore methods in drug discovery. J Med Chem 2010;53:539–58.CrossrefPubMedGoogle Scholar

  • 19.

    Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 2006;20:647–71.CrossrefPubMedGoogle Scholar

  • 20.

    Barnum D, Greene J, Smellie A, Sprague P. Identification of common functional configurations among molecules. J Chem Inf Comput Sci 1996;36:563–71.PubMedCrossrefGoogle Scholar

  • 21.

    Richmond NJ, Abrams CA, Wolohan PR, Abrahamian E, Willet P, Clark RD. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput AidedMol Des 2006;20:567–87.Google Scholar

  • 22.

    Jones G, Willett P, Glen RC. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 1995;9:532–49.CrossrefPubMedGoogle Scholar

  • 23.

    Chemical Computing Group. Molecular operating environment. Montreal, Canada: Chemical Computing Group (http://www.chemcomp.com/).

  • 24.

    Murcko MA. Recent advances in ligand design methods. Rev Comput Chem 1997;11:1–66.Google Scholar

  • 25.

    Clark DE, Murray CW, Li J. Current issues in de novo molecular design. Rev Comput Chem 1997;11:67–125.Google Scholar

  • 26.

    Lengauer T, Rarey M. Computational methods for biomolecular docking. Curr Opin Struct Biol 1996;6:402–6.PubMedCrossrefGoogle Scholar

  • 27.

    Böhm HJ, Stahl M. Structure-based library design: molecular modelling merges with combinatorial chemistry. Curr Opin Chem Biol 2000;4:283–6.CrossrefPubMedGoogle Scholar

  • 28.

    Jaim AN. Effects of protein conformation in docking: improved pose prediction though protein pocket adaptation. J Comput Aided Mol Des 2009;23:355–74Google Scholar

  • 29.

    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739–49.Google Scholar

  • 30.

    McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK. Gaussian docking functions. Biopolymers 2003;68:76–90.CrossrefPubMedGoogle Scholar

  • 31.

    Ewing TJ, Kuntz ID. Critical evaluation of search algorithms for automated molecular docking and database screening. J Comput Chem 1997;18:1175–89.CrossrefGoogle Scholar

  • 32.

    Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996;261:470–89.CrossrefPubMedGoogle Scholar

  • 33.

    Welch W, Ruppert J, Jain AN. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 1996;3:449–62.PubMedCrossrefGoogle Scholar

  • 34.

    Irwin JJ, Shoichet BK. ZINC – a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005;45:177–82.CrossrefPubMedGoogle Scholar

  • 35.

    Faraldo-Gómez JD, Roux B. Characterization of conformational equilibria through Hamiltonian and temperature replica-exchange simulations: assessing entropic and environmental effects. J Comput Chem 2007;28:1634–47.CrossrefGoogle Scholar

  • 36.

    Glen RC, Allen SC. Ligand-protein docking: cancer research at the interface between biology and chemistry. Curr Med Chem 2003;10:763–77.CrossrefPubMedGoogle Scholar

  • 37.

    Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004;3:935–49.PubMedCrossrefGoogle Scholar

  • 38.

    Huang S-Y, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci 2010;11:3016–34.CrossrefPubMedGoogle Scholar

  • 39.

    Huang S-Y, Zou X. Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions. J Chem Inf Model 2010;50:262–73.CrossrefPubMedGoogle Scholar

  • 40.

    Berman HM, Bhat TN, Bourne PE, Feng ZK, Gilliland G, Weissig H, et al. The protein data bank and the challenge of structural genomics. Nat Struct Biol 2000;7:957–9.PubMedCrossrefGoogle Scholar

  • 41.

    Westbrook J, Feng ZK, Chen L, Yang HW, Berman HM. The protein data bank and structural genomics. Nucleic Acid Res 2003;31:489–91.CrossrefGoogle Scholar

  • 42.

    Scherzer-Attali R, Pellarin R, Convertino M, Frydman-Marom A, Egoz-Matia N, Peled S, et al. Complete phenotypic recovery of an Alzheimer’s disease model by a quinone-tryptophan hybrid aggregation inhibitor. PloS One 2010;5:e11101.CrossrefGoogle Scholar

  • 43.

    Takahashi T, Ohta K, Mihara H. Rational design of amyloid β peptide-binding proteins: pseudo-Aβ β sheet surface presented in green fluorescent protein binds tightly and preferentially to structured Aβ. Proteins 2010;78:336–47.PubMedCrossrefGoogle Scholar

  • 44.

    Huang H, Chen Q, Ku X, Meng L, Lin L, Wang X, et al. A series of α-heterocyclic carboxaldehyde thiosemicarbazones inhibit topoisomerase II α catalytic activity. J Med Chem 2010;53: 3048–64.CrossrefPubMedGoogle Scholar

  • 45.

    Murphy EA, Shields DJ, Stoletov K, Dneprovskaia E, McElroy M, Greenberg JI, et al. Disruption of angiogenesis and tumor growth with an orally active drug that stabilizes the inactive state of PDGFRβ/B-RAF. Proc Natl Acad Sci USA 2010;107: 4299–304.CrossrefGoogle Scholar

  • 46.

    Liu JJ, Zeng NH, Zhang LR, Zhan YY, Chen Y, Wang YA, et al. A unique pharmacophore for activation of the nuclear orphan receptor Nur77 in vivo and in vitro. Cancer Res 2010;70: 3628–37.CrossrefGoogle Scholar

  • 47.

    Kozikowski AP, Cho SJ, Jensen NH, Allen JA, Svennebring AM, Roth BL. HTS and rational drug design to generate a class of 5-HT2C-selective ligands for possible use in schizophrenia. Chem Med Chem 2010;5:1221–5.CrossrefGoogle Scholar

  • 48.

    Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 2007;152:21–37.PubMedCrossrefGoogle Scholar

  • 49.

    Coupez B, Lewis RA. Docking and scoring. Theoretically easy, practically impossible? Curr Med Chem 2006;13:2995–3003.PubMedGoogle Scholar

  • 50.

    Geppert T, Bauer S, Hiss JA, Conrad E, Reutlinger M, Schneider P, et al. Immunosuppressive small molecule discovered by structure-based virtual screening for inhibitors of protein-protein interactions. Angew Chem Int Ed Engl 2012;51: 258–61.CrossrefPubMedGoogle Scholar

  • 51.

    Cournia Z, Leng L, Gandavadi S, Du X, Bucala R, Jorgensen WL. Discovery of human macrophage migration inhibitory factor (MIF)-CD74 antagonists via virtual screening. J Med Chem 2009;52:416–24.PubMedCrossrefGoogle Scholar

  • 52.

    Barreiro G, Kim JT, Guimaraes CR, Bailey CM, Domaoal RA, Wang L, et al. From docking false positive to active anti-HIV agent. J Med Chem 2007;50:5324–9.PubMedCrossrefGoogle Scholar

  • 53.

    van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003;2: 192–204.CrossrefGoogle Scholar

  • 54.

    Hodgson J. ADMET – turning chemical into drugs. Nat Biotechnol 2001;19:722–6.CrossrefGoogle Scholar

  • 55.

    Gleeson P, Hersey A, Hannongbua S. In silico ADME models: a general assessment of their utility in drug discovery applications. Curr Topics Med Chem 2011;11:358–81.CrossrefGoogle Scholar

  • 56.

    Jorgensen WL. Efficient drug lead discovery and optimization. Acc Chem Res 2009;42:724–33.CrossrefPubMedGoogle Scholar

  • 57.

    Wang HW, Duffy RA, Boykow GC, Chackalamannil S, Madison VS. Identification of novel cannabinoid CB1 receptor antagonists by using virtual screening with a pharmacophore model. J Med Chem 2008;51:2439–46.Google Scholar

  • 58.

    Rella M, Rushworth CA, Guy JL, Turner AJ, Langer T, Jackson RM. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. J Chem Inf Model 2006;46:708–16.CrossrefPubMedGoogle Scholar

  • 59.

    Clackers M, Coe DM, Demaine DA, Hardy GW, Humphreys D, Inglis GG, et al. Non-steroidal glucocorticoid agonist. The discovery of aryl pyrazoles as A-ring mimetics. Bioorg Med Chem Lett 2007;17:4737–45.CrossrefPubMedGoogle Scholar

  • 60.

    Taft CA, Da Silva VB, Da Silva CH. Current topics in computer-aided drug design. J Pharm Sci 2008;97:1089–98.CrossrefGoogle Scholar

  • 61.

    Shukla S, Choubey SK, Srivastava P, Gomase VS. Chemoinformatics – an emerging field for computer aided drug design. J Biotechnol Lett 2010;1:10–4.Google Scholar

  • 62.

    Rao VS, Srinivas K. Modern drug discovery process: an in silico approach. J Bioinform Seq Anal 2011;2:89–94.Google Scholar

  • 63.

    Wang JA, Hewick RM. Proteomics in drug discovery. Drug Discov Today 1999;4:129–33.CrossrefPubMedGoogle Scholar

  • 64.

    Cusick ME, Klitgord N, Vidal M, Hill DE. Interactome: gateway into systems biology. Hum Mol Genet 2005;2: 171–81.CrossrefGoogle Scholar

  • 65.

    Gurwitz D. Cataloging the interactome of small molecules and the human proteome. Drug Dev Res 2011;72:1–3.CrossrefGoogle Scholar

About the article

Corresponding author: María Kimatrai Salvador, Departamento de Química Farmacéutica y Orgánica, Facultad de Farmacia, Universidad de Granada, Campus de Cartuja s/n Granada, 18071, España

Received: 2012-06-07

Accepted: 2012-10-05

Published Online: 2012-11-09

Published in Print: 2012-12-01

Citation Information: Drug Metabolism and Drug Interactions, Volume 27, Issue 4, Pages 199–207, ISSN (Online) 2191-0162, ISSN (Print) 0792-5077, DOI: https://doi.org/10.1515/dmdi-2012-0021.

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