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Review

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

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Pages 81-92 | Published online: 11 Nov 2014

References

  • Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod. 2007;70(3):461–477.
  • Lourenço AM, Ferreira LM, Branco PS. Molecules of natural origin, semi-synthesis and synthesis with anti-inflammatory and anticancer utilities. Curr Pharm Des. 2012;18(26):3979–4046.
  • Wikberg JES, Spjuth O, Eklund M, Lapins M. Chemoinformatics Taking Biology into Account: Proteochemometrics. In: Guha R, Bender A, editors. Computational Approaches in Cheminformatics and Bioinformatics. Hoboken: John Wiley & Sons; 2011:57–92.
  • Reardon S. Project ranks billions of drug interactions. Nature. 2013; 503(7477):449–450.
  • Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–1249.
  • Krasavin M, Karapetian R, Konstantinov I, et al. Discovery and potency optimization of 2-amino-5-arylmethyl-1,3-thiazole derivatives as potential therapeutic agents for prostate cancer. Arch Pharm (Weinheim). 2009;342(7):420–427.
  • Kaul P. . Drug discovery: Past, present and future. In: Jucker E, editor. Progress in Drug Research, Volume 50. Berlin: Springer Science and Business Media; 1998:9–105.
  • Veselovsky AV, Zharkova MS, Poroikov VV, Nicklaus MC. Computer-aided design and discovery of protein-protein interaction inhibitors as agents for anti-HIV therapy. SAR QSAR Environ Res. 2014;25(6):457–471.
  • Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform. 2009;10(5):579–591.
  • Taft CA, Da Silva VB, Da Silva CH. Current topics in computer-aided drug design. J Pharm Sci. 2008;97(3):1089–1098.
  • Bajorath J. Integration of virtual and high-throughput screening. Nat Rev Drug Discov. 2002;1(11):882–894.
  • Hopkins AL, Keserü GM, Leeson PD, Rees DC, Reynolds CH. The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov. 2014;13(2):105–121.
  • Ballester PJ, Mangold M, Howard NI, et al. Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface. 2012;9(77):3196–3207.
  • Boyd MR. The position of intellectual property rights in drug discovery and development from natural products. J Ethnopharmacol. 1996;51(1–3):17–25; discussion 25–27.
  • Thiel KA. Structure-aided drug design’s next generation. Nat Biotechnol. 2004;22(5):513–519.
  • Schuffenhauer A. Computational methods for scaffold hopping. Wiley Interdiscip Rev Comput Mol Sci. 2012;2(6):842–867.
  • Sun H, Tawa G, Wallqvist A. Classification of scaffold-hopping approaches. Drug Discov Today. 2012;17(7–8):310–324.
  • Schneider G, Schneider P, Renner S. Scaffold-Hopping: How Far Can You Jump? QSAR Comb Sci. 2006;25(12):1162–1171.
  • Langdon SR, Westwood IM, van Montfort RL, Brown N, Blagg J. Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. J Chem Inf Model. 2013;53(5):1100–1112.
  • Li AP. Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today. 2001;6(7):357–366.
  • Yu H, Adedoyin A. ADME–Tox in drug discovery: integration of experimental and computational technologies. Drug Discov Today. 2003;8(18):852–861.
  • Ekins S, Boulanger B, Swaan PW, Hupcey MA. Towards a new age of virtual ADME/TOX and multidimensional drug discovery. Mol Divers. 2000;5(4):255–275.
  • Agrafiotis DK, Bandyopadhyay D, Wegner JK, Vlijmen Hv. Recent advances in chemoinformatics. J Chem Inf Model. 2007;47(4):1279–1293.
  • Valerio LG Jr, Choudhuri S. Chemoinformatics and chemical genomics: potential utility of in silico methods. J Appl Toxicol. 2012;32(11):880–889.
  • Hong H, Xie Q, Ge W, et al. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model. 2008;48(7):1337–1344.
  • Vogt M, Bajorath J. Chemoinformatics: a view of the field and current trends in method development. Bioorg Med Chem. 2012;20(18):5317–5323.
  • Kapetanovic IM. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact. 2008;171(2):165–176.
  • Karelson M, Lobanov VS, Katritzky AR. Quantum-Chemical Descriptors in QSAR/QSPR Studies. Chem Rev. 1996;96(3):1027–1044.
  • Gozalbes R, Doucet JP, Derouin F. Application of topological descriptors in QSAR and drug design: history and new trends. Curr Drug Targets Infect Disord. 2002;2(1):93–102.
  • Perkins R, Fang H, Tong W, Welsh WJ. Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology. Environ Toxicol Chem. 2003;22(8):1666–1679.
  • 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(11):935–949.
  • 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(4):671–680.
  • 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(6):795–799.
  • Kharkar PS, Warrier S, Gaud RS. Reverse docking: a powerful tool for drug repositioning and drug rescue. Future Med Chem. 2014;6(3):333–342.
  • Lee M, Kim D. Large-scale reverse docking profiles and their applications. BMC Bioinformatics. 2012;13:S6.
  • Ivanciuc O. . Drug Design with Artificial Intelligence Methods. In: Meyers RA, editor. Encyclopedia of Complexity and Systems Science. Berlin: Springer; 2009:2113–2139.
  • Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007;13(14):1497–1508.
  • Shin WJ, Seong BL. Recent advances in pharmacophore modeling and its application to anti-influenza drug discovery. Expert Opin Drug Discov. 2013;8(4):411–426.
  • Braga RC, Andrade CH. Assessing the performance of 3D pharmacophore models in virtual screening: how good are they? Curr Top Med Chem. 2013;13(9):1127–1138.
  • Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today. 2010;15(11–12):444–450.
  • Güner O, Clement O, Kurogi Y. Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Curr Med Chem. 2004;11(22):2991–3005.
  • Krautscheid Y, Senning CJÅ, Sartori SB, et al. Pharmacophore modeling, virtual screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin receptors ligands. J Chem Inf Model. 2014;54(6):1747–1757.
  • Ehrlich P. Über den jetzigen Stand der Chemotherapie. Ber Dtsch Chem Ges. 1909;42(1):17–47.
  • Schueler FW. Chemobiodynamics and Drug Design. New York; McGraw-Hill: 1960.
  • Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem. 1998;70:1129–1143.
  • Gund P. . Evolution of the pharmacophore Concept in Pharmaceutical Research. In: Güner OF, editor. Pharmacophore Perception, Development, and Use in Drug Design. La Jolla: Internat’l University Line.
  • McGregor MJ, Muskal SM. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci. 1999;39(3):569–574.
  • McGregor MJ, Muskal SM. Pharmacophore fingerprinting. 2. Application to primary library design. J Chem Inf Comput Sci. 2000;40(1):117–125.
  • Mason JS, Morize I, Menard PR, Cheney DL, Hulme C, Labaudiniere RF. New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J Med Chem. 1999;42(17):3251–3264.
  • Geppert TD, Lipsky PE. Antigen presentation at the inflammatory site. Crit Rev Immunol. 1989;9(4):313–362.
  • Sheridan RP, Rusinko A 3rd, Nilakantan R, Venkataraghavan R. Searching for pharmacophores in large coordinate data bases and its use in drug design. Proc Natl Acad Sci U S A. 1989;86(20):8165–8169.
  • Jones G, Willett P, Glen RC. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des. 1995;9(6):532–549.
  • Goto J, Kataoka R, Hirayama N. Ph4Dock: pharmacophore-based protein-ligand docking. J Med Chem. 2004;47(27):6804–6811.
  • Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today. 2008;13(1–2):23–29.
  • Langer T, Hoffman RD. Pharmacophores and Pharmacophore Searches. Mannhold R, Kubinyi H, Folkers G, editors. Hoboken: John Wiley & Sons; 2006:395.
  • Liu X, Zhu F, Ma XH, et al. Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. Curr Med Chem. 2013;20(13):1646–1661.
  • Thai KM, Ngo TD, Tran TD, Le MT. Pharmacophore modeling for antitargets. Curr Top Med Chem. 2013;13(9):1002–1014.
  • Luu TT, Malcolm N, Nadassy K. Pharmacophore modeling methods in focused library selection – applications in the context of a new classification scheme. Comb Chem High Throughput Screen. 2011;14(6):488–499.
  • Jose RA, Voet A, Broos K, et al. An integrated fragment based screening approach for the discovery of small molecule modulators of the VWF-GPIbalpha interaction. Chem Commun (Camb). 2012;48(92):11349–11351.
  • Voet AR, Kumar A, Berenger F, Zhang KY. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des. 2014;28(4):363–373.
  • Hähnke V, Schneider G. Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching. J Comput Chem. 2011;32(8):1635–1647.
  • Catalyst (r). Vol San Diego: Accelrys, Inc.; 2014. Available from: http://accelrys.com/products/discovery-studio/pharmacophore-ligand-based-design.html. Accessed September 5, 2014.
  • Sanders MPA, McGuire R, Roumen L, et al. From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Commun. 2012;3:28–38.
  • Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–169.
  • Desaphy J, Azdimousa K, Kellenberger E, Rognan D. Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. J Chem Inf Model. 2012;52(8):2287–2299.
  • Böhm HJ. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des. 1992;6(1):61–78.
  • Barillari C, Marcou G, Rognan D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model. 2008;48(7):1396–1410.
  • Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem. 1985;28(7):849–857.
  • Tintori C, Corradi V, Magnani M, Manetti F, Botta M. Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery. J Chem Inf Model. 2008;48(11):2166–2179.
  • Voet A, Helsen C, Zhang KY, Claessens F. The discovery of novel human androgen receptor antagonist chemotypes using a combined pharmacophore screening procedure. ChemMedChem. 2013;8(4):644–651.
  • Helsen C, Van den Broeck T, Voet A, et al. Androgen receptor antagonists for prostate cancer therapy. Endocr Relat Cancer. 2014;21(4): T105–T118.
  • Kumar A, Zhang KYJ. Hierarchical virtual screening approaches in small molecule drug discovery. Methods. Epub July 27, 2014.
  • Dunbar JB Jr, Smith RD, Yang CY, et al. CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. J Chem Inf Model. 2011;51(9):2036–2046.
  • Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model. 2013;53(8):1853–1870.
  • Hindle SA, Rarey M, Buning C, Lengaue T. Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des. 2002;16(2):129–149.
  • Hu B, Lill MA. Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model. 2012;52(4):1046–1060.
  • Hu B, Lill MA. PharmDock: a pharmacophore-based docking program. J Cheminform. 2014;6:14.
  • Mobley DL, Liu S, Lim NM, et al. Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28(4):327–345.
  • Lin JH, Lu AY. Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol Rev. 1997;49(4):403–449.
  • Alavijeh MS, Palmer AM. The pivotal role of drug metabolism and pharmacokinetics in the discovery and development of new medicines. I Drugs. 2004;7(8):755–763.
  • Guner OF, Bowen JP. Pharmacophore modeling for ADME. Curr Top Med Chem. 2013;13(11):1327–1342.
  • Yamashita F, Hashida M. In silico approaches for predicting ADME properties of drugs. Drug Metab Pharmacokinet. 2004;19(5):327–338.
  • Tanaka E. Clinically important pharmacokinetic drug-drug interactions: role of cytochrome P450 enzymes. J Clin Pharm Ther. 1998; 23(6):403–416.
  • de Groot MJ, Ekins S. Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev. 2002;54(3):367–383.
  • Ekins S, de Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab Dispos. 2001;29(7):936–944.
  • Masimirembwa CM, Ridderström M, Zamora I, Andersson TB. Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates. Methods Enzymol. 2002;357:133–144.
  • Schuster D, Laggner C, Steindl TM, Langer T. Development and validation of an in silico P450 profiler based on pharmacophore models. Curr Drug Discov Technol. 2006;3(1):1–48.
  • Sorich MJ, Miners JO, McKinnon RA, Smith PA. Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol. 2004;65(2):301–308.
  • Sorich MJ, Smith PA, McKinnon RA, Miners JO. Pharmacophore and quantitative structure activity relationship modelling of UDP-glucuronosyltransferase 1A1 (UGT1A1) substrates. Pharmacogenetics. 2002;12(8):635–645.
  • Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25(2):197–206.
  • Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics. 2011;74(12):2554–2574.
  • Rollinger JM, Schuster D, Danzl B, et al. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med. 2009;75(3):195–204.
  • Hu Y, Bajorath J. Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model. 2010;50(12):2112–2118.
  • Scior T, Bender A, Tresadern G, et al. Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model. 2012;52(4):867–881.
  • Kirchmair J, Wolber G, Laggner C, Langer T. Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model. 2006;46(4):1848–1861.
  • Kirchmair J, Laggner C, Wolber G, Langer T. Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model. 2005;45(2):422–430.
  • De Luca L, Barreca ML, Ferro S, et al. Pharmacophore-based discovery of small-molecule inhibitors of protein-protein interactions between HIV-1 integrase and cellular cofactor LEDGF/p75. ChemMedChem. 2009;4(8):1311–1316.
  • Christ F, Voet A, Marchand A, et al. Rational design of small-molecule inhibitors of the LEDGF/p75-integrase interaction and HIV replication. Nat Chem Biol. 2010;6(6):442–448.
  • Vancraenenbroeck R, De Raeymaecker J, Lobbestael E, et al. In silico, in vitro and cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci. 2014;7:51.
  • 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(6):1676–1686.
  • Kumar A, Voet A, Zhang KY. Fragment based drug design: from experimental to computational approaches. Curr Med Chem. 2012;19(30):5128–5147.
  • Böhm HJ. A novel computational tool for automated structure-based drug design. J Mol Recognit. 1993;6(3):131–137.
  • Lippert T, Schulz-Gasch T, Roche O, Guba W, Rarey M. De novo design by pharmacophore-based searches in fragment spaces. J Comput Aided Mol Des. 2011;25(10):931–945.
  • Cavalluzzo C, Voet A, Christ F, et al. De novo design of small molecule inhibitors targeting the LEDGF/p75-HIV integrase interaction. RSC Adv. 2012;2:974.
  • Cavalluzzo C, Christ F, Voet A, et al. Identification of small peptides inhibiting the integrase-LEDGF/p75 interaction through targeting the cellular co-factor. J Pept Sci. 2013;19(10):651–658.
  • Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature. 2007;450(7172):1001–1009.
  • Wilson AJ. Inhibition of protein-protein interactions using designed molecules. Chem Soc Rev. 2009;38:3289–3300.
  • Fry DC. Drug-like inhibitors of protein-protein interactions: a structural examination of effective protein mimicry. Curr Protein Pept Sci. 2008;9(3):240–247.
  • Voet A, Berenger F, Zhang KY. Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors. PLoS One. 2013;8(10):e75762.
  • Voet A, Zhang KY. Pharmacophore modelling as a virtual screening tool for the discovery of small molecule protein-protein interaction inhibitors. Curr Pharm Des. 2012;18(30):4586–4598.
  • Voet A, Banwell EF, Sahu KK, Heddle JG, Zhang KY. Protein interface pharmacophore mapping tools for small molecule protein: protein interaction inhibitor discovery. Curr Top Med Chem. 2013;13(9):989–1001.
  • Reddy TR, Li C, Fischer PM, Dekker LV. Three-dimensional pharmacophore design and biochemical screening identifies substituted 1,2,4-triazoles as inhibitors of the annexin A2-S100A10 protein interaction. ChemMedChem. 2012;7(8):1435–1446.
  • Voet A, Callewaert L, Ulens T, et al. Structure based discovery of small molecule suppressors targeting bacterial lysozyme inhibitors. Biochem Biophys Res Commun. 2011;405(4):527–532.
  • Mustata G, Li M, Zevola N, et al. Development of small-molecule PUMA inhibitors for mitigating radiation-induced cell death. Curr Top Med Chem. 2011;11(3):281–290.
  • Voet ARD, Akihiro I, Hirohama M, et al. Discovery of small molecule inhibitors targeting the SUMO–SIM interaction using a protein interface consensus approach. Med Chem Commun. 2014;5:783–786.
  • Corradi V, Mancini M, Manetti F, Petta S, Santucci MA, Botta M. Identification of the first non-peptidic small molecule inhibitor of the c-Abl/14-3-3 protein-protein interactions able to drive sensitive and Imatinib-resistant leukemia cells to apoptosis. Bioorg Med Chem Lett. 2010;20(20):6133–6137.
  • Baker D. Centenary Award and Sir Frederick Gowland Hopkins Memorial Lecture. Protein folding, structure prediction and design. Biochem Soc Trans. 2014;42(2):225–229.
  • Tinberg CE, Khare SD, Dou J, et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature. 2013;501(7466):212–216.
  • Nivón LG, Moretti R, Baker D. A Pareto-optimal refinement method for protein design scaffolds. PLoS One. 2013;8:e59004.