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Computer-Aided Drug Discovery pp 167–188Cite as

Pharmacophore Modeling: Methods and Applications

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Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

A pharmacophore represents the essential features of a molecular interaction and are an integral part of modern computational drug discovery. This review provides an introduction into the basic concepts and approaches of pharmacophore-based drug design using a practical example. Recently developed approaches and tools for utilizing pharmacophores are also reviewed.

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References

  1. Braga RC, Andrade CH (2013) Assessing the performance of 3d pharmacophore models in virtual screening: how good are they? Curr Top Med Chem 13(9):1127–1138

    Article  CAS  PubMed  Google Scholar 

  2. Guner O, Clement O, Kurogi Y (2004) Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Curr Med Chem 11(22):2991–3005

    Article  PubMed  Google Scholar 

  3. Chemical Computing Group (2015) Molecular Operating Environment (MOE) version 2013.08. Chemical Computing Group, Inc.

    Google Scholar 

  4. Dixon S, Smondyrev A, Knoll E, Rao S, Shaw D, Friesner R (2006) 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 20(10):647–671. doi:10.1007/s10822-006-9087-6, PubMed:17124629

    Article  CAS  PubMed  Google Scholar 

  5. Leach AR, Gillet VJ, Lewis RA, Taylor R (2009) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53(2):539–558. doi:10.1021/jm900817u, PubMed:19831387

    Article  Google Scholar 

  6. Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11–12):444–450

    Article  CAS  PubMed  Google Scholar 

  7. Dragos H (2011) Pharmacophore-based virtual screening. In: Bajorath J (ed) Chemoinformatics and computational chemical biology, vol 672, Methods in molecular biology. Humana Press, New York, pp 261–298

    Google Scholar 

  8. Mason JS, Good AC, Martin EJ (2001) 3-D pharmacophores in drug discovery. Curr Pharm Des 7(7):567–597

    Article  CAS  PubMed  Google Scholar 

  9. Langer T, Krovat EM (2003) Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr Opin Drug Discov Devel 6(3):370–376, PubMed:12833670

    CAS  PubMed  Google Scholar 

  10. Seidel T, Ibis G, Bendix F, Wolber G (2010) Strategies for 3D pharmacophore-based virtual screening: 3D pharmacophore elucidation and virtual screening. Drug Discov Today Technol 7(4):e221–e228

    Article  CAS  Google Scholar 

  11. Van Drie JH (2013) Generation of three-dimensional pharmacophore models. WIREs Comput Mol Sci 3(5):449–464

    Article  CAS  Google Scholar 

  12. Martin YC (2007) 4.06 - Pharmacophore modeling: 1 methods. In: Comprehensive medicinal chemistry {II}. Elsevier, Oxford, pp 119–147

    Chapter  Google Scholar 

  13. Chen GG, Zeng Q, Tse GMK (2008) Estrogen and its receptors in cancer. Med Res Rev 28(6):954–974

    Article  CAS  PubMed  Google Scholar 

  14. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. doi:10.1021/jm300687e, PubMed:22716043. PubMed Central:PMC3405771

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. RDKit: open-source cheminformatics. http://www.rdkit.org. Accessed 4 Sep 2012

  16. Ebejer J-P, Morris GM, Deane C (2012) Freely available conformer generation methods: how good are they? J Chem Inf Model. doi:10.1021/ci2004658, PubMed:22482737

    PubMed  Google Scholar 

  17. Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM (1992) Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114(25):10024–10035

    Article  CAS  Google Scholar 

  18. Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51(6):1307–1314. doi:10.1021/ci200097m, PubMed:21604800. PubMed Central:PMC3124593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Güner OF, Phillip Bowen J (2014) Setting the record straight: the origin of the pharmacophore concept. J Chem Inf Model 54(5):1269–1283

    Article  PubMed  Google Scholar 

  20. Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70(5):1129

    Article  CAS  Google Scholar 

  21. Hou X, Du J, Liu R, Zhou Y, Li M, Xu W, Fang H (2015) Enhancing the sensitivity of pharmacophore-based virtual screening by incorporating customized zbg features: a case study using histone deacetylase 8. J Chem Inf Model 55(4):861–871

    Article  CAS  PubMed  Google Scholar 

  22. Koes D, Khoury K, Huang Y, Wang W, Bista M, Popowicz GM, Wolf S, Holak TA, Dömling A, Camacho CJ (2012) Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS One 7(3):e32839 EP. doi:10.1371/journal.pone.0032839, PubMed:22427896. PubMed Central:PMC3299697

    Article  Google Scholar 

  23. Anstead GM, Carlson KE, Katzenellenbogen JA (1997) The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site. Steroids 62(3):268–303, PubMed:9071738

    Article  CAS  PubMed  Google Scholar 

  24. Sanders MPA, Barbosa AJM, Zarzycka B, Nicolaes GAF, Klomp JPG, de Vlieg J, Del Rio A (2012) Comparative analysis of pharmacophore screening tools. J Chem Inf Model 52(6):1607–1620

    Article  CAS  PubMed  Google Scholar 

  25. Spitzer GM, Heiss M, Mangold M, Markt P, Kirchmair J, Wolber G, Liedl KR (2010) One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J Chem Inf Model 50(7):1241–1247. doi:10.1021/ci100136b, PubMed:20583761

    Article  CAS  PubMed  Google Scholar 

  26. Wolber G, Seidel T, Bendix F, Langer T (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13(1–2):23–29. doi:10.1016/j.drudis.2007.09.007, Epub 2007 Nov 5

    Article  CAS  PubMed  Google Scholar 

  27. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33. doi:10.1186/1758-2946-3-33, PubMed:21982300. PubMed Central:PMC3198950

    Article  PubMed  PubMed Central  Google Scholar 

  28. Daylight theory manual, daylight version 4.9 ed. Feb 2008. Daylight Chemical Information Systems, Inc., Aliso Viejo, CA

    Google Scholar 

  29. Jones G (2010) Gape: an improved genetic algorithm for pharmacophore elucidation. J Chem Inf Model 50(11):2001–2018

    Article  CAS  PubMed  Google Scholar 

  30. Jones G, Willet P, Glen R (2000) GASP: genetic algorithm superimposition program. In: Güner OF (ed) Pharmacophore perception, development, and use in drug design, vol 2. International University Line, La Jolla, CA, pp 85–106

    Google Scholar 

  31. Gardiner EJ, Cosgrove DA, Taylor R, Gillet VJ (2009) Multiobjective optimization of pharmacophore hypotheses: bias toward low-energy conformations. J Chem Inf Model 49(12):2761–2773

    Article  CAS  PubMed  Google Scholar 

  32. Taylor R, Cole JC, Cosgrove DA, Gardiner EJ, Gillet VJ, Korb O (2012) Development and validation of an improved algorithm for overlaying flexible molecules. J Comput Aided Mol Des 26(4):451–472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Moser D, Wittmann SK, Kramer J, Blöcher R, Achenbach J, Pogoryelov D, Proschak E (2015) Peng: a neural gas-based approach for pharmacophore elucidation. method design, validation, and virtual screening for novel ligands of lta4h. J Chem Inf Model 55(2):284–293

    Article  CAS  PubMed  Google Scholar 

  34. Korb O, Monecke P, Hessler G, Stützle T, Exner TE (2010) Pharmacophore: multiple flexible ligand alignment based on ant colony optimization. J Chem Inf Model 50(9):1669–1681

    Article  CAS  PubMed  Google Scholar 

  35. Binns M, de Visser SP, Theodoropoulos C (2012) Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching. J Chem Inf Model 52(2):577–588

    Article  CAS  PubMed  Google Scholar 

  36. Artese A, Cross S, Costa G, Distinto S, Parrotta L, Alcaro S, Ortuso F, Cruciani G (2013) Molecular interaction fields in drug discovery: recent advances and future perspectives. WIREs Comput Mol Sci 3(6):594–613

    Article  CAS  Google Scholar 

  37. Cross S, Baroni M, Goracci L, Cruciani G (2012) Grid-based three-dimensional pharmacophores i: Flappharm, a novel approach for pharmacophore elucidation. J Chem Inf Model 52(10):2587–2598

    Article  CAS  PubMed  Google Scholar 

  38. Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ (2008) PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res 36:W223–W228. doi:10.1093/nar/gkn187, PubMed:18424800. PubMed Central:PMC2447755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. The PyMOL Molecular Graphics System, Version 1.6. Schrödinger, LLC, August. http://www.pymol.org/

  40. Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ (2008) Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules. J Comput Biol 15(7):737–754. doi:10.1089/cmb.2007.0130, PubMed:18662104. PubMed Central:PMC2699263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Giangreco I, Cosgrove DA, Packer MJ (2013) An extensive and diverse set of molecular overlays for the validation of pharmacophore programs. J Chem Inf Model 53(4):852–866

    Article  CAS  PubMed  Google Scholar 

  42. Cross S, Ortuso F, Baroni M, Costa G, Distinto S, Moraca F, Alcaro S, Cruciani G (2012) Grid-based three-dimensional pharmacophores ii: Pharmbench, a benchmark data set for evaluating pharmacophore elucidation methods. J Chem Inf Model 52(10):2599–2608

    Article  CAS  PubMed  Google Scholar 

  43. Patel Y, Gillet VJ, Bravi G, Leach AR (2002) A comparison of the pharmacophore identification programs: catalyst, DISCO and GASP. J Comput Aided Mol Des 16(8):653–681, PubMed:12602956

    Article  CAS  PubMed  Google Scholar 

  44. Tiikkainen P, Markt P, Wolber G, Kirchmair J, Distinto S, Poso A, Kallioniemi O (2009) Critical comparison of virtual screening methods against the MUV data set. J Chem Inf Model 49(10):2168–2178. doi:10.1021/ci900249b, PubMed:19799417

    Article  CAS  PubMed  Google Scholar 

  45. Manepalli S, Geffert LM, Surratt CK, Madura JD (2011) Discovery of novel selective serotonin reuptake inhibitors through development of a protein-based pharmacophore. J Chem Inf Model 51(9):2417–2426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sanders MPA, Verhoeven S, de Graaf C, Roumen L, Vroling B, Nabuurs SB, de Vlieg J, Klomp JPG (2011) Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs. J Chem Inf Model 51(9):2277–2292

    Article  CAS  PubMed  Google Scholar 

  47. Klabunde T, Giegerich C, Evers A (2009) Sequence-derived three-dimensional pharmacophore models for g-protein-coupled receptors and their application in virtual screening. J Med Chem 52(9):2923–2932

    Article  CAS  PubMed  Google Scholar 

  48. Sanders MPA, McGuire R, Roumen L, de Esch IJP, de Vlieg J, Klomp JPG, de Graaf C (2012) From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Commun 3:28–38

    Article  CAS  Google Scholar 

  49. Bingjie H, Lill MA (2013) Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. J Chem Inf Model 53(5):1179–1190

    Article  Google Scholar 

  50. Rafał K, Bojarski AJ (2013) New strategy for receptor-based pharmacophore query construction: a case study for 5-ht7 receptor ligands. J Chem Inf Model 53(12):3233–3243

    Article  Google Scholar 

  51. Wenbo Y, Lakkaraju SK, Raman EP, MacKerell AD Jr (2014) Site-identification by ligand competitive saturation (silcs) assisted pharmacophore modeling. J Comput Aided Mol Des 28(5):491–507

    Article  Google Scholar 

  52. Wenbo Y, Lakkaraju SK, Raman EP, Fang L, MacKerell AD (2015) Pharmacophore modeling using site-identification by ligand competitive saturation (silcs) with multiple probe molecules. J Chem Inf Model 55(2):407–420

    Article  Google Scholar 

  53. Bingjie H, Lill MA (2012) Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model. doi:10.1021/ci200620h, PubMed:22397751. PubMed Central:PMC3422394

    Google Scholar 

  54. Grove LE, Hall DR, Beglov D, Vajda S, Kozakov D (2013) Ftflex: accounting for binding site flexibility to improve fragment-based identification of druggable hot spots. Bioinformatics 29(9):1218–1219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ross GA, Morris GM, Biggin PC (2012) Rapid and accurate prediction and scoring of water molecules in protein binding sites. PLoS One 7(3):e32036. doi:10.1371/journal.pone.0032036, PubMed:22396746. PubMed Central:PMC3291545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE, Luo L, Xia B, Beglov D, Vajda S (2015) The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc 10(5):733–755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wolber G, Langer T (2004) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169. doi:10.1021/ci049885e, PubMed:15667141

    Article  Google Scholar 

  58. Meslamani J, Li J, Sutter J, Stevens A, Bertrand HO, Rognan D (2012) Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. J Chem Inf Model 52(4):943–955

    Article  CAS  PubMed  Google Scholar 

  59. Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40:W409–W414. doi:10.1093/nar/gks378, PubMed:22553363. PubMed Central:PMC3394271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Salam NK, Nuti R, Sherman W (2009) Novel method for generating structure-based pharmacophores using energetic analysis. J Chem Inf Model 49(10):2356–2368

    Article  CAS  PubMed  Google Scholar 

  61. Bowman AL, Makriyannis A (2011) Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (faah). J Chem Inf Model 51(12):3247–3253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Mason JS, Morize I, Menard PR, Cheney DL, Hulme C, Labaudinieres RF (1999) 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 42(17):3251–3264. doi:10.1021/jm9806998, PubMed:10464012

    Article  CAS  PubMed  Google Scholar 

  63. Mason JS, Cheney DL (2000) Library design and virtual screening using multiple 4 point pharmacophore fingerprints. Pac Symp Biocomput 5:576–587, PubMed:10902205

    Google Scholar 

  64. Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and application. J Chem Inf Model 47(2):279–294. doi:10.1021/ci600253e, PubMed:17381166

    Article  CAS  PubMed  Google Scholar 

  65. Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein-ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53(3):623–637

    Article  CAS  PubMed  Google Scholar 

  66. Awale M, Reymond J-L (2014) Atom pair 2d-fingerprints perceive 3d-molecular shape and pharmacophores for very fast virtual screening of zinc and gdb-17. J Chem Inf Model 54(7):1892–1907

    Article  CAS  PubMed  Google Scholar 

  67. Hähnke V, Schneider G (2011) Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching. J Comput Chem 32(8):1635–1647

    Article  PubMed  Google Scholar 

  68. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63

    Article  PubMed  Google Scholar 

  69. Wood DJ, de Vlieg J, Wagener M, Ritschel T (2012) Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. J Chem Inf Model 52(8):2031–2043

    Article  CAS  PubMed  Google Scholar 

  70. Desaphy J, Azdimousa K, Kellenberger E, Rognan D (2012) Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. J Chem Inf Model 52(8):2287–2299

    Article  CAS  PubMed  Google Scholar 

  71. Raymond JW, Willett P (2002) Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J Comput Aided Mol Des 16(7):521–533, PubMed:12510884

    Article  CAS  PubMed  Google Scholar 

  72. Dror O, Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2009) Novel approach for efficient pharmacophore-based virtual screening: method and applications. J Chem Inf Model 49(10):2333–2343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Vilar S, Tatonetti NP, Hripcsak G (2015) 3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance. Sci Rep 5

    Google Scholar 

  74. Wang X, Chen H, Yang F, Gong J, Li S, Pei J, Liu X, Jiang H, Lai L, Li H (2014) idrug: a web-accessible and interactive drug discovery and design platform. J Cheminform 6(1):28

    Article  PubMed  PubMed Central  Google Scholar 

  75. iDrug - an online interactive drug discovery and design platform. http://lilab.ecust.edu.cn/idrug. Accessed 4 May 2015

  76. Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (2010) PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 38:W609–W614. doi:10.1093/nar/gkq300, PubMed:20430828. PubMed Central:PMC2896160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. AnchorQuery. http://anchorquery.csb.pitt.edu. Accessed 4 May 2015

  78. Ryan Koes D, Camacho CJ (2012) PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Res. doi:10.1093/nar/gks336, PubMed:22523085. PubMed Central:PMC3394328

    Google Scholar 

  79. ZINCPharmer. http://zincpharmer.csb.pitt.edu. Accessed 4 May 2015

  80. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768. doi:10.1021/ci3001277, Epub 2012 Jun 15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Drwal MN, Griffith R (2013) Combination of ligand- and structure-based methods in virtual screening. Drug Discov Today Technol 10(3):e395–e401

    Article  PubMed  Google Scholar 

  82. Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53(8):1893–1904

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461, http://www.ncbi.nlm.nih.gov/pubmed/19499576

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Planesas JM, Claramunt RM, Teixidó J, Borrell JI, Pérez-Nueno VI (2011) Improving vegfr-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing. J Chem Inf Model 51(4):777–787

    Article  CAS  PubMed  Google Scholar 

  85. Thangapandian S, John S, Sakkiah S, Lee KW (2011) Molecular docking and pharmacophore filtering in the discovery of dual-inhibitors for human leukotriene a4 hydrolase and leukotriene c4 synthase. J Chem Inf Model 51(1):33–44

    Article  CAS  PubMed  Google Scholar 

  86. Moustakas DT, Lang PT, Pegg S, Pettersen E, Kuntz ID, Brooijmans N, Rizzo RC (2006) Development and validation of a modular, extensible docking program: Dock 5. J Comput Aided Mol Des 20(10-11):601–619

    Article  CAS  PubMed  Google Scholar 

  87. Jiang L, Rizzo RC (2015) Pharmacophore-based similarity scoring for dock. J Phys Chem B 119(3):1083–1102

    Article  CAS  PubMed  Google Scholar 

  88. Kramer C, Gedeck P (2010) Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets. J Chem Inf Model 50(11):1961–1969, 10.1021/ci100264e

    Article  CAS  PubMed  Google Scholar 

  89. Certara. SYBYL-X. http://www.certara.com/products/molmod/sybyl-x/

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Acknowledgements

We would like to thank Lee McDermott, Dan Zuckerman, and Jocelyn Sunseri for their insightful feedback during the preparation of the manuscript. This work was supported by the National Institute of Health [R01GM108340]. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.

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  1. Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    David Ryan Koes

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  1. Chemistry Department, Southern Research Institute, Birmingham, Alabama, USA

    Wei Zhang

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Koes, D.R. (2015). Pharmacophore Modeling: Methods and Applications. In: Zhang, W. (eds) Computer-Aided Drug Discovery. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/7653_2015_46

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  • DOI: https://doi.org/10.1007/7653_2015_46

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