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Molecular Docking Methodologies

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Biomolecular Simulations

Part of the book series: Methods in Molecular Biology ((MIMB,volume 924))

Abstract

Molecular docking represents an important technology for structure-based drug design. Docking is a computational technique aimed at the prediction of the most favorable ligand–target spatial configuration and an estimate of the corresponding complex free energy, although as stated at the beginning accurate scoring methods remain still elusive. In this chapter, the state of art of molecular docking methodologies and their applications in drug discovery is summarized.

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References

  1. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  PubMed  CAS  Google Scholar 

  2. Lipinski C, Hopkins A (2004) Navigating chemical space for biology and medicine. Nature 432:855–861

    Article  PubMed  CAS  Google Scholar 

  3. Muller G (2003) Medicinal chemistry of target family-directed masterkeys. Drug Discov Today 8:681–691

    Article  PubMed  Google Scholar 

  4. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857

    Article  PubMed  CAS  Google Scholar 

  5. DesJarlais RL, Sheridan RP, Seibel GL, Dixon JS, Kuntz ID, Venkataraghavan R (1988) Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure. J Med Chem 31:722–729

    Article  PubMed  CAS  Google Scholar 

  6. Milletti F, Storchi L, Sforna G, Cross S, Cruciani G (2009) Tautomer enumeration and stability prediction for virtual screening on large chemical databases. J Chem Inf Model 49:68–75

    Article  PubMed  CAS  Google Scholar 

  7. Milletti F, Storchi L, Sforna G, Cruciani G (2007) New and original pKa prediction method using grid molecular interaction fields. J Chem Inf Model 47:2172–2181

    Article  PubMed  CAS  Google Scholar 

  8. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  PubMed  CAS  Google Scholar 

  9. Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245:43–53

    Article  PubMed  CAS  Google Scholar 

  10. Baxter CA, Murray CW, Clark DE, Westhead DR, Eldridge MD (1998) Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins 33:367–382

    Article  PubMed  CAS  Google Scholar 

  11. Hall SB, Venkitaraman AR, Whitsett JA, Holm BA, Notter RH (1992) Importance of hydrophobic apoproteins as constituents of clinical exogenous surfactants. Am Rev Respir Dis 145:24–30

    Article  PubMed  CAS  Google Scholar 

  12. 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 (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  PubMed  CAS  Google Scholar 

  13. Goto J, Kataoka R, Hirayama N (2004) Ph4Dock: pharmacophore-based protein-ligand docking. J Med Chem 47:6804–6811

    Article  PubMed  CAS  Google Scholar 

  14. 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 47:279–294

    Article  PubMed  CAS  Google Scholar 

  15. Sciabola S, Stanton RV, Mills JE, Flocco MM, Baroni M, Cruciani G, Perruccio F, Mason JS (2010) High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields. J Chem Inf Model 50:155–169

    Article  PubMed  CAS  Google Scholar 

  16. Carosati E, Budriesi R, Ioan P, Ugenti MP, Frosini M, Fusi F, Corda G, Cosimelli B, Spinelli D, Chiarini A, Cruciani G (2008) Discovery of novel and cardioselective diltiazem-like calcium channel blockers via virtual screening. J Med Chem 51:5552–5565

    Article  PubMed  CAS  Google Scholar 

  17. Carosati E, Mannhold R, Wahl P, Hansen JB, Fremming T, Zamora I, Cianchetta G, Baroni M (2007) Virtual screening for novel openers of pancreatic K(ATP) channels. J Med Chem 50:2117–2126

    Article  PubMed  CAS  Google Scholar 

  18. Park K, Kim D (2006) A method to detect important residues using protein binding site comparison. Genome Inform 17:216–225

    PubMed  CAS  Google Scholar 

  19. Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  PubMed  CAS  Google Scholar 

  20. Congreve M, Chessari G, Tisi D, Woodhead AJ (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    Article  PubMed  CAS  Google Scholar 

  21. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236

    Article  CAS  Google Scholar 

  22. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445

    Article  PubMed  CAS  Google Scholar 

  23. Verkhivker GM (2004) Computational analysis of ligand binding dynamics at the intermolecular hot spots with the aid of simulated tempering and binding free energy calculations. J Mol Graph Model 22:335–348

    Article  PubMed  CAS  Google Scholar 

  24. Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11–26

    Article  PubMed  CAS  Google Scholar 

  25. Muegge I (2006) PMF scoring revisited. J Med Chem 49:5895–5902

    Article  PubMed  CAS  Google Scholar 

  26. Velec HF, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48:6296–6303

    Article  PubMed  CAS  Google Scholar 

  27. Bohm HJ (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8:243–256

    Article  PubMed  CAS  Google Scholar 

  28. Ballesteros JA, Weinstein H (1995) Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci London 25:366–366

    Article  CAS  Google Scholar 

  29. Huang N, Jacobson MP (2007) Physics-based methods for studying protein-ligand interactions. Curr Opin Drug Discov Devel 10:325–331

    PubMed  CAS  Google Scholar 

  30. Gouda H, Kuntz ID, Case DA, Kollman PA (2003) Free energy calculations for theophylline binding to an RNA aptamer: Comparison of MM-PBSA and thermodynamic integration methods. Biopolymers 68:16–34

    Article  PubMed  CAS  Google Scholar 

  31. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE 3rd (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897

    Article  PubMed  CAS  Google Scholar 

  32. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48:4040–4048

    Article  PubMed  CAS  Google Scholar 

  33. Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391

    Article  PubMed  CAS  Google Scholar 

  34. Aqvist J, Luzhkov VB, Brandsdal BO (2002) Ligand binding affinities from MD simulations. Acc Chem Res 35:358–365

    Article  PubMed  Google Scholar 

  35. Jones-Hertzog DK, Jorgensen WL (1997) Binding affinities for sulfonamide inhibitors with human thrombin using Monte Carlo simulations with a linear response method. J Med Chem 40:1539–1549

    Article  PubMed  CAS  Google Scholar 

  36. Bortolato A, Moro S (2007) In silico binding free energy predictability by using the linear interaction energy (LIE) method: bromobenzimidazole CK2 inhibitors as a case study. J Chem Inf Model 47:572–582

    Article  PubMed  CAS  Google Scholar 

  37. Stjernschantz E, Marelius J, Medina C, Jacobsson M, Vermeulen NP, Oostenbrink C (2006) Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? An evaluation of the linear interaction energy (LIE) method. J Chem Inf Model 46:1972–1983

    Article  PubMed  CAS  Google Scholar 

  38. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  PubMed  CAS  Google Scholar 

  39. Stubbs MT, Reyda S, Dullweber F, Moller M, Klebe G, Dorsch D, Mederski WW, Wurziger H (2002) pH-dependent binding modes observed in trypsin crystals: lessons for structure-based drug design. Chembiochem 3:246–249

    Article  PubMed  CAS  Google Scholar 

  40. Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084

    Article  PubMed  CAS  Google Scholar 

  41. Mark AE, van Gunsteren WF (1994) Decomposition of the free energy of a system in terms of specific interactions Implications for theoretical and experimental studies. J Mol Biol 240:167–176

    Article  PubMed  CAS  Google Scholar 

  42. Dill KA (1997) Additivity principles in biochemistry. J Biol Chem 272:701–704

    PubMed  CAS  Google Scholar 

  43. Tirado-Rives J, Jorgensen WL (2006) Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J Med Chem 49:5880–5884

    Article  PubMed  CAS  Google Scholar 

  44. Garman E, Laver G (2004) Controlling influenza by inhibiting the virus’s neuraminidase. Curr Drug Targets 5:119–136

    Article  PubMed  CAS  Google Scholar 

  45. von Itzstein M, Wu WY, Kok GB, Pegg MS, Dyason JC, Jin B, Van Phan T, Smythe ML, White HF, Oliver SW et al (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–423

    Article  Google Scholar 

  46. Kaldor SW, Kalish VJ, Davies JF 2nd, Shetty BV, Fritz JE, Appelt K, Burgess JA, Campanale KM, Chirgadze NY, Clawson DK, Dressman BA, Hatch SD, Khalil DA, Kosa MB, Lubbehusen PP, Muesing MA, Patick AK, Reich SH, Su KS, Tatlock JH (1997) Viracept (nelfinavir mesylate, AG1343): a potent, orally bioavailable inhibitor of HIV-1 protease. J Med Chem 40:3979–3985

    Article  PubMed  CAS  Google Scholar 

  47. Chen H, Lyne PD, Giordanetto F, Lovell T, Li J (2006) On evaluating molecular-docking methods for pose prediction and enrichment factors. J Chem Inf Model 46:401–415

    Article  PubMed  CAS  Google Scholar 

  48. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  PubMed  CAS  Google Scholar 

  49. Murray CW, Callaghan O, Chessari G, Cleasby A, Congreve M, Frederickson M, Hartshorn MJ, McMenamin R, Patel S, Wallis N (2007) Application of fragment screening by X-ray crystallography to beta-secretase. J Med Chem 50:1116–1123

    Article  PubMed  CAS  Google Scholar 

  50. Huang JW, Zhang Z, Wu B, Cellitti JF, Zhang X, Dahl R, Shiau CW, Welsh K, Emdadi A, Stebbins JL, Reed JC, Pellecchia M (2008) Fragment-based design of small molecule X-linked inhibitor of apoptosis protein inhibitors. J Med Chem 51:7111–7118

    Article  PubMed  CAS  Google Scholar 

  51. Fink T, Bruggesser H, Reymond JL (2005) Virtual exploration of the small-molecule chemical universe below 160 Daltons. Angew Chem Int Ed Engl 44:1504–1508

    Article  PubMed  CAS  Google Scholar 

  52. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16:3–50

    Article  PubMed  CAS  Google Scholar 

  53. Chen Y, Shoichet BK (2009) Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 5:358–364

    Article  PubMed  CAS  Google Scholar 

  54. Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733

    Article  PubMed  CAS  Google Scholar 

  55. Brown SP, Muchmore SW (2006) High-throughput calculation of protein-ligand binding affinities: modification and adaptation of the MM-PBSA protocol to enterprise grid computing. J Chem Inf Model 46:999–1005

    Article  PubMed  CAS  Google Scholar 

  56. Fujitani H, Tanida Y, Ito M, Jayachandran G, Snow CD, Shirts MR, Sorin EJ, Pande VS (2005) Direct calculation of the binding free energies of FKBP ligands. J Chem Phys 123:084108

    Article  PubMed  Google Scholar 

  57. Fedorov DG, Kitaura K (2007) Extending the power of quantum chemistry to large systems with the fragment molecular orbital method. J Phys Chem A 111:6904–6914

    Article  PubMed  CAS  Google Scholar 

  58. Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 49:4805–4808

    Article  PubMed  CAS  Google Scholar 

  59. Yang CY, Sun H, Chen J, Nikolovska-Coleska Z, Wang S (2009) Importance of ligand reorganization free energy in protein-ligand binding-affinity prediction. J Am Chem Soc 131:13709–13721

    Article  PubMed  CAS  Google Scholar 

  60. Gloriam DE, Fredriksson R, Schioth HB (2007) The G protein-coupled receptor subset of the rat genome. BMC Genomics 8:338

    Article  PubMed  Google Scholar 

  61. Marinissen MJ, Gutkind JS (2001) G-protein-coupled receptors and signaling networks: emerging paradigms. Trends Pharmacol Sci 22:368–376

    Article  PubMed  CAS  Google Scholar 

  62. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5:993–996

    Article  PubMed  CAS  Google Scholar 

  63. Strange PG (2008) Signaling mechanisms of GPCR ligands. Curr Opin Drug Discov Devel 11:196–202

    PubMed  CAS  Google Scholar 

  64. Kolb P, Rosenbaum DM, Irwin JJ, Fung JJ, Kobilka BK, Shoichet BK (2009) Structure-based discovery of beta2-adrenergic receptor ligands. Proc Natl Acad Sci USA 106:6843–6848

    Article  PubMed  CAS  Google Scholar 

  65. Sabio M, Jones K, Topiol S (2008) Use of the X-ray structure of the beta2-adrenergic receptor for drug discovery. Part 2: Identification of active compounds. Bioorg Med Chem Lett 18:5391–5395

    Article  PubMed  CAS  Google Scholar 

  66. de Graaf C, Rognan D (2008) Selective structure-based virtual screening for full and partial agonists of the beta2 adrenergic receptor. J Med Chem 51:4978–4985

    Article  PubMed  Google Scholar 

  67. Katritch V, Reynolds KA, Cherezov V, Hanson MA, Roth CB, Yeager M, Abagyan R (2009) Analysis of full and partial agonists binding to beta2-adrenergic receptor suggests a role of transmembrane helix V in agonist-specific conformational changes. J Mol Recognit 22:307–318

    Article  PubMed  CAS  Google Scholar 

  68. Katritch V, Jaakola VP, Lane JR, Lin J, Ijzerman AP, Yeager M, Kufareva I, Stevens RC, Abagyan R (2010) Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J Med Chem 53:1799–1809

    Article  PubMed  CAS  Google Scholar 

  69. Carlsson J, Yoo L, Gao ZG, Irwin JJ, Shoichet BK, Jacobson KA (2010) Structure-based discovery of A2A adenosine receptor ligands. J Med Chem 53:3748–3755

    Article  PubMed  CAS  Google Scholar 

  70. Michino M, Abola E, Brooks CL 3rd, Dixon JS, Moult J, Stevens RC (2009) Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat Rev Drug Discov 8:455–463

    Article  PubMed  CAS  Google Scholar 

  71. Moro S, Deflorian F, Bacilieri M, Spalluto G (2006) Ligand-based homology modeling as attractive tool to inspect GPCR structural plasticity. Curr Pharm Des 12:2175–2185

    Article  PubMed  CAS  Google Scholar 

  72. Moro S, Deflorian F, Spalluto G, Pastorin G, Cacciari B, Kim SK, Jacobson KA (2003) Demystifying the three dimensional structure of G protein-coupled receptors (GPCRs) with the aid of molecular modeling. Chem Commun (Camb) 24:2949–2956

    Article  Google Scholar 

  73. Maconi A, Pastorin G, Da Ros T, Spalluto G, Gao ZG, Jacobson KA, Baraldi PG, Cacciari B, Varani K, Moro S, Borea PA (2002) Synthesis, biological properties, and molecular modeling investigation of the first potent, selective, and water-soluble human A(3) adenosine receptor antagonist. J Med Chem 45:3579–3582

    Article  PubMed  CAS  Google Scholar 

  74. Pastorin G, Da Ros T, Spalluto G, Deflorian F, Moro S, Cacciari B, Baraldi PG, Gessi S, Varani K, Borea PA (2003) Pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine derivatives as adenosine receptor antagonists. Influence of the N5 substituent on the affinity at the human A 3 and A 2B adenosine receptor subtypes: a molecular modeling investigation. J Med Chem 46:4287–4296

    Article  PubMed  CAS  Google Scholar 

  75. Colotta V, Catarzi D, Varano F, Calabri FR, Lenzi O, Filacchioni G, Martini C, Trincavelli L, Deflorian F, Moro S (2004) 1,2,4-triazolo[4,3-a]quinoxalin-1-one moiety as an attractive scaffold to develop new potent and selective human A3 adenosine receptor antagonists: synthesis, pharmacological, and ligand-receptor modeling studies. J Med Chem 47:3580–3590

    Article  PubMed  CAS  Google Scholar 

  76. Moro S, Braiuca P, Deflorian F, Ferrari C, Pastorin G, Cacciari B, Baraldi PG, Varani K, Borea PA, Spalluto G (2005) Combined target-based and ligand-based drug design approach as a tool to define a novel 3D-pharmacophore model of human A3 adenosine receptor antagonists: pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine derivatives as a key study. J Med Chem 48:152–162

    Article  PubMed  CAS  Google Scholar 

  77. Gao ZG, Chen A, Barak D, Kim SK, Muller CE, Jacobson KA (2002) Identification by site-directed mutagenesis of residues involved in ligand recognition and activation of the human A3 adenosine receptor. J Biol Chem 277:19056–19063

    Article  PubMed  CAS  Google Scholar 

  78. Evers A, Gohlke H, Klebe G (2003) Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials. J Mol Biol 334:327–345

    Article  PubMed  CAS  Google Scholar 

  79. Cavasotto CN, Orry AJ, Murgolo NJ, Czarniecki MF, Kocsi SA, Hawes BE, O‘Neill KA, Hine H, Burton MS, Voigt JH, Abagyan RA, Bayne ML, Monsma FJ Jr (2008) Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 51:581–588

    Article  PubMed  CAS  Google Scholar 

  80. Kimura SR, Tebben AJ, Langley DR (2008) Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach. Proteins 71:1919–1929

    Article  PubMed  CAS  Google Scholar 

  81. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci USA 99:12562–12566

    Article  PubMed  CAS  Google Scholar 

  82. Masetti M, Cavalli A, Recanatini M, Gervasio FL (2009) Exploring complex protein-ligand recognition mechanisms with coarse metadynamics. J Phys Chem B 113:4807–4816

    Article  PubMed  CAS  Google Scholar 

  83. Gervasio FL, Laio A, Parrinello M (2005) Flexible docking in solution using metadynamics. J Am Chem Soc 127:2600–2607

    Article  PubMed  CAS  Google Scholar 

  84. Vargiu AV, Ruggerone P, Magistrato A, Carloni P (2008) Dissociation of minor groove binders from DNA: insights from metadynamics simulations. Nucleic Acids Res 36:5910–5921

    Article  PubMed  CAS  Google Scholar 

  85. Provasi D, Bortolato A, Filizola M (2009) Exploring molecular mechanisms of ligand recognition by opioid receptors with metadynamics. Biochemistry 48:10020–10029

    Article  PubMed  CAS  Google Scholar 

  86. Pearlstein RA, Hu QY, Zhou J, Yowe D, Levell J, Dale B, Kaushik VK, Daniels D, Hanrahan S, Sherman W, Abel R (2010) New hypotheses about the structure-function of proprotein convertase subtilisin/kexin type 9: analysis of the epidermal growth factor-like repeat A docking site using WaterMap. Proteins 78:2571–2586

    PubMed  CAS  Google Scholar 

  87. Guimaraes CR, Mathiowetz AM (2010) Addressing limitations with the MM-GB/SA scoring procedure using the WaterMap method and free energy perturbation calculations. J Chem Inf Model 50:547–559

    Article  PubMed  CAS  Google Scholar 

  88. Chrencik JE, Patny A, Leung IK, Korniski B, Emmons TL, Hall T, Weinberg RA, Gormley JA, Williams JM, Day JE, Hirsch JL, Kiefer JR, Leone JW, Fischer HD, Sommers CD, Huang HC, Jacobsen EJ, Tenbrink RE, Tomasselli AG, Benson TE (2010) Structural and thermodynamic characterization of the TYK2 and JAK3 kinase domains in complex with CP-690550 and CMP-6. J Mol Biol 400:413–433

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Heptares Therapeutics Ltd, Hertfordshire, UK

    Andrea Bortolato & Jonathan S. Mason

  2. Dipartimento di Scienze Farmaceutiche, Universita di Padova, Padova, Italy

    Marco Fanton & Stefano Moro

  3. Lundbeck A/S, Copenhagen, Denmark

    Jonathan S. Mason

Authors
  1. Andrea Bortolato
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  2. Marco Fanton
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  3. Jonathan S. Mason
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  4. Stefano Moro
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Correspondence to Stefano Moro .

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Editors and Affiliations

  1. DSIMB, UMR-S 665, INSERM, rue Alexandre Cabanel 6, Paris, 75015, France

    Luca Monticelli

  2. , Department of Applied Physics, Aalto University, Otakaari, AALTO, FI-00076, Finland

    Emppu Salonen

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Bortolato, A., Fanton, M., Mason, J.S., Moro, S. (2013). Molecular Docking Methodologies. In: Monticelli, L., Salonen, E. (eds) Biomolecular Simulations. Methods in Molecular Biology, vol 924. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-017-5_13

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  • DOI: https://doi.org/10.1007/978-1-62703-017-5_13

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