Skip to main content

Molecular Modeling: Considerations for the Design of Pharmaceuticals and Biopharmaceuticals

  • Chapter
Biopharmaceutical Drug Design and Development

Abstract

In this chapter, we provide the reader with a broad overview of the tools and techniques commonly used in the fields of molecular modeling and computer-assisted drug design (CADD) and at the same time present context with respect to the discovery, design, and development of therapeutic agents. Note is also made to some of the challenges that lie at the cutting edge of these disciplines. We highlight a number of techniques and related software programs as they apply to the development of therapeutic pharmaceutical and biopharmaceutical agents.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Levinthal, C. (1966) Molecular model-building by computer. Sci. Am. 214, 42–52.

    Article  CAS  PubMed  Google Scholar 

  2. Barry, C. D., Ellis, R. A., Graesser, S., and Marshall, G. R. (1969) Display and manipulation in three dimensions, In: Pertinent Concepts in Computer Graphics (Faiman, M. and Nievergelt, J., eds.), University of Illinois Press, Chicago, pp. 104–153.

    Google Scholar 

  3. Richardson, J. R. (1981) The anatomy and taxonomy of protein structure. Adv. Protein Chem. 34, 167–339.

    Article  CAS  PubMed  Google Scholar 

  4. Carson, M. and Bugg, C. E. (1986) Algorithm for ribbon models of proteins. J. Mol. Graph. 4, 121–122.

    Article  CAS  Google Scholar 

  5. Connolly, M. L. (1985) Molecular surface triangulation. J. Appl. Cryst. 18, 499–505.

    Article  CAS  Google Scholar 

  6. Brickmann, J., Exner, T. E., Keil, M., and Marhöfer, R. J. (2000) Molecular graphicstrends and perspectives. J. Mol. Model. 6, 328–340.

    Article  CAS  Google Scholar 

  7. Shreiner, D. (2004) OpenGL Programming Guide: The Official Reference Document to OpenGL, version 1.4. 4th ed., Addison-Wesley, Boston.

    Google Scholar 

  8. Pearlman, R. S. (1987) Rapid Generation of High Quality Approximate 3D Molecular Structures. Chem. Des. Automat. News 2, 5–7.

    Google Scholar 

  9. Klebe, G. and Meitzner, T. (1994) A fast and efficient method to generate biologically relevant compounds. J. Comput. Aided Mol. Des. 8, 583–606.

    Article  CAS  PubMed  Google Scholar 

  10. Allinger, N. L. (1977) Conformational Analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc. 99, 8127–8134.

    Article  CAS  Google Scholar 

  11. Allinger, N. L., Kuohsiang, C., and Lii, J.-H. (1996) An improved force field (MM4) for saturated hydrocarbons. J. Comput. Chem. 17, 642–668.

    Article  CAS  Google Scholar 

  12. Allinger, N. L., Yuh, Y. H., and Lii, J.-H. (1989) Molecular mechanics. The MM3 force field for hydrocarbons. J. Am. Chem. Soc. 111, 8551–8566.

    Article  CAS  Google Scholar 

  13. Halgren, T. A. (1996) Merck molecular force filed. I. Basis, form, scope, parameterization and performance of MMFF94. J. Comput. Chem. 17, 490–519.

    Article  CAS  Google Scholar 

  14. Clark, M., Cramer, III R. D., and van Opdenhosch, N. (1989) Validation of the general purpose tripos 5.2 force field. J. Comput. Chem. 10, 982–1012.

    Article  CAS  Google Scholar 

  15. Pearlman, D. A., Case, D. A., Caldwell, J. W., et al. (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91, 1–41.

    Article  CAS  Google Scholar 

  16. MacKerell, A. D., Jr., Bashford, D., Bellott, M., et al. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586–3617.

    Article  CAS  Google Scholar 

  17. Gogonea, V. and Merz, K. M. Jr. (1999) Fully quantum mechanical description of proteins in solution. Combining linear scaling quantum mechanical methodologies with the Poisson-Boltzman equation. J. Phys. Chem. A 103, 5171–5188.

    Article  CAS  Google Scholar 

  18. Schlick, T. and Gan, H. H., eds. (2002) Computational methods for macromolecules: challenges and applications: proceedings of the 3rd international workshop on algorithms for molecular modeling, New York, October 12–14, 2000, Springer, New York.

    Google Scholar 

  19. Hammett, L. P. (1940) Physical Organic Chemistry, McGraw-Hill, New York.

    Google Scholar 

  20. Taft, R. W. (1956) Chapter 13. In: Steric Effects in Organic Chemistry (Neuman, M. S., ed.), Wiley, New York, p. 556.

    Google Scholar 

  21. Hansch, C. and Fujita, T. (1964) Rho-sigma-pi Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86, 1616–1626.

    Article  CAS  Google Scholar 

  22. Hansch, C., Maloney, P. P., Fujita, T., and Muir, R. M. (1962) Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature (London) 194, 178–180.

    Article  CAS  Google Scholar 

  23. Jurs, P. C., Chow, J. T., and Yuan, M. (1979) Studies of chemical structure-biological activity relations using pattern recognition. In: Computer-Assisted Drug Design (Olson, E. C. and Christoffersen, R. E., eds.), The American Chemical Society, Washington, DC, pp. 103–129.

    Chapter  Google Scholar 

  24. Stuper, A. J., Brugger, W. E., and Jurs, P. C. (1979) Computer-Assisted Studies of Chemical Structure and Biological Function. John Wiley & Sons, New York.

    Google Scholar 

  25. Karelson, M. (2000) Molecular Descriptors in QSAR/QSPR, John Wiley and Sons, Inc., New York.

    Google Scholar 

  26. Todeschini, R. and Consonni, V. (2000) Handbook of Molecular Descriptors. Wiley-VCH Verlag GmbH, Weinheim.

    Book  Google Scholar 

  27. Randic, M. (1975) On characterization of molecular branching. J. Am. Chem. Soc. 97(23), 6609–6615.

    Article  CAS  Google Scholar 

  28. Hall, L. H., Kier, L. B., and Murray, W. J. (1975) Molecular connectivity 2: relationship to water solubility and boiling point. J. Pharm. Sci. 64(12), 1974–1977.

    Article  CAS  PubMed  Google Scholar 

  29. Kier, L. B. and Hall, L. H. (1976) Molecular Connectivity 7: Specific Treatment of Heteroatoms. J. Pharm. Sci. 65(12), 1806–1809.

    Article  CAS  PubMed  Google Scholar 

  30. Kier, L. B., Hall, L. H., Murray, W. J., and Randic, M. (1975) Molecular connectivity 1: relationship to nonspecific local anesthesia. J. Pharm. Sci. 64(12), 1971–1974.

    Article  CAS  PubMed  Google Scholar 

  31. Kier, L. B., Murray, W. J., and Hall, L. H. (1975) Molecular connectivity. 4. relationships to biological activities. J. Med. Chem. 18(12), 1272–1274.

    Article  CAS  PubMed  Google Scholar 

  32. Murray, W. J., Hall, L. H., and Kier, L. B. (1975) Molecular connectivity 3: relationship to partition coefficients. J. Pharm. Sci. 64(12), 1978–1981.

    Article  CAS  PubMed  Google Scholar 

  33. Murray, W. J., Hall, L. H., and Kier, L. B. (1976) Molecular connectivity 5: connectivity series applied to density. J. Pharm. Sci. 65(8), 1226–1230.

    Article  PubMed  Google Scholar 

  34. Murray, W. J., Kier, L. B., and Hall, L. H. (1976) Molecular connectivity. 6. examination of the parabolic relationship between molecular connectivity and biological activity. J. Med. Chem. 19(5), 573–578.

    Article  CAS  PubMed  Google Scholar 

  35. Kier, L. B. and Hall, L. H. (1990) An electrotopological state index for atoms in molecules. Pharm. Res. 7(8), 801–807.

    Article  CAS  PubMed  Google Scholar 

  36. Goldstein, H., Poole, C. P., and Safko, J. L. (2002) Classical Mechanics, 3rd ed., Addison Wesley, San Francisco.

    Google Scholar 

  37. Pearlman, R. S. (1980) Molecular surface areas and volumes and their use in structure/activity relationships. In: Physical Chemical Properties of Drugs (Yalkowsky, S. H., Sinkula, A. A., and Valvani, S. C., eds.), Marcel Dekker, New York.

    Google Scholar 

  38. Miller, K. J. and Savchik, J. A. (1979) A new empirical method to calculate average molecular polarizibilities. J. Am. Chem. Soc. 101(24), 7206–7213.

    Article  CAS  Google Scholar 

  39. Stanton, D. T. and Jurs, P. C. (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structureproperty relationship studies. Anal. Chem. 62, 2323–2329.

    Article  CAS  Google Scholar 

  40. Pearlman, R. S. and Smith, K. M. (1998) Novel software tools for chemical diversity. Perspect. Drug Discov. Des. 9, 339–353.

    Article  Google Scholar 

  41. Cramer, III R. D., Patterson, D. E., and Bunce, J. D. (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959–5967.

    Article  CAS  Google Scholar 

  42. Kearsley, S. K. and Smith, G. M. (1990) An alternative method for the alignment of molecular structures: maximizing electrostatic and steric overlap. Tetrahedron Comput. Methodol. 3(6), 615–633.

    Article  CAS  Google Scholar 

  43. Klebe, G., Abraham, U., and Mietzner, T. (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 37(24), 4130–4146.

    Article  CAS  PubMed  Google Scholar 

  44. Crippen, G. M. (1981) Distance Geometry and Conformational Calculations. Research Studies Press, New York.

    Google Scholar 

  45. Crippen, G. M. (1979) Distance geometry approach to rationalizing binding data. J. Med. Chem. 22(8), 988–997.

    Article  CAS  PubMed  Google Scholar 

  46. Ghose, A. K. and Crippen, G. M. (1982) Quantitative structure-activity relationship by distance geometry: quinazolines as dihydrofolate reductase inhibitors. J. Med. Chem. 25(8), 892–899.

    Article  CAS  PubMed  Google Scholar 

  47. Hopfinger, A. J. (1981) Inhibition of dihydrofolate reductase: structure-activity correlations of 2,4-diamino-5-benzylpyrimidines based upon molecular shape analysis. J. Med. Chem. 24(7), 818–822.

    Article  CAS  PubMed  Google Scholar 

  48. Hopfinger, A. J. (1980) A QSAR investigation of dihydrofolate reductase inhibition by Baker Triazines based upon molecular shape analysis. J. Am. Chem. Soc. 102(24), 7196–7206.

    Article  CAS  Google Scholar 

  49. Hopfinger, A. J. (1983) Theory and application of molecular potential energy fields in molecular shape analysis: a quantitative structure-activity relationship study of 2,4-diamino-5-benzylpyrimidines as dihydrofolate reductase inhibitors. J. Med. Chem. 26(7), 990–996.

    Article  CAS  PubMed  Google Scholar 

  50. Doweyko, A. (2004) 3D-QSAR Illusions. J. Comput. Aided Mol. Des. 18, 587–596.

    Article  CAS  PubMed  Google Scholar 

  51. Clark, D. E. (2005) Computational prediction of ADMET properties: recent developments and future challenges. In: Annual Reports in Computational Chemistry (Spellmeyer, D. C., Carlson, H., Crawford, T. D., et al., eds.), Elsevier, Amsterdam, pp. 133–151.

    Google Scholar 

  52. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5–14.

    Article  CAS  PubMed  Google Scholar 

  53. Mosier, P. D. and Jurs, P. C. (2002) QSAR/QSPR Studies using probabilistic neural networks and generalized regression neural networks. J. Chem. Inf. Comput. Sci. 42, 1460–1470.

    CAS  PubMed  Google Scholar 

  54. Kier, L. B. and Hall, L. H. (2002) The meaning of molecular connectivity. Croat. Chem. Acta 75, 371–382.

    CAS  Google Scholar 

  55. Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., and Gramatica, P. (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Env. Health Perspect. 111, 1361–1375.

    CAS  Google Scholar 

  56. Guha, R. and Jurs, P. C. (2005) Determining the validity of a QSAR Model — a classification approach. J. Chem. Inf. Model 45, 65–73.

    Article  CAS  PubMed  Google Scholar 

  57. Frederique, B. and Dragos, H. (2004) Molecular similarity and property similarity. Curr. Topics Med. Chem. 4, 589–600.

    Article  Google Scholar 

  58. Lipinski, C. A. (2005) Filtering in drug discovery. In: Annual Reports in Computational Chemistry (Spellmeyer, D. C., Carlson, H., Crawford, T. D., et al., eds.), Elsevier, Amsterdam, pp. 155–168.

    Google Scholar 

  59. Ghose, A. K. and Viswanadhan, V. N., eds. (2001) Combinatorial Library Design and Evaluation. Marcel Dekker, New York/Basel.

    Google Scholar 

  60. Berman, H. M., Battistuz, T., Bhat, T. N., et al. (2002) The protein data bank. Acta Cryst. D 58, 899–907.

    Article  CAS  Google Scholar 

  61. Böhm, H.-J. (1995) Site-directed structure generation by fragment-joining. Perspect. Drug Discov. Des. 3, 21–33.

    Article  Google Scholar 

  62. Dixon, J. S., Blaney, J. M., and Weininger, D. (1993) Characterizing and satisfying the steric and chemical restraints of binding sites. In: The Third York Meeting, pp. 29–30.

    Google Scholar 

  63. Payne, A. W. R. and Glen, R. C. (1993) Molecular recognition using a binary genetic search algorithm. J. Mol. Graph. 11, 74–91.

    Article  CAS  PubMed  Google Scholar 

  64. Bartlett, P. A. (1986) Design of enzyme inhibitors: answering biological questions through organic synthesis. In: Organic Synthesis, From Gnosis to Prognosis (NATO Advanced Study Institute) (Chatgilialoglu, C. and Snieckus, V., eds.), Kluwer Academic Publishers, Dordrecht, pp. 137–173.

    Google Scholar 

  65. Lauri, G. and Bartlett, P. A. (1994) CAVEAT — a program to facilitate the design of organic molecules. J. Comput. Aided Mol. Des. 8, 51–66.

    Article  CAS  PubMed  Google Scholar 

  66. DesJarlais, R. L., Sheridan, R. P., Seibel, G. L., Dixon, J. S., Kuntz, I. D., and 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  CAS  PubMed  Google Scholar 

  67. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., and Ferrin, T. E. (1982) A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 161, 269–288.

    Article  CAS  PubMed  Google Scholar 

  68. Meng, E. C., Schoichet, B. K., and Kuntz, I. D. (1993) Automated docking with grid-based energy evaluation. J. Comput. Chem. 13, 505–524.

    Article  Google Scholar 

  69. Kramer, B., Metz, G., Rarey, M., and Lengauer, T. (1999) Ligand docking and screening with FlexX. Med. Chem. Res. 9, 463–478.

    CAS  Google Scholar 

  70. Rarey, M., Kramer, B., and Lengauer, T. (1997) Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J Comput. Aided Mol. Des. 11, 369–384.

    Article  CAS  PubMed  Google Scholar 

  71. Rarey, M., Kramer, B., Lengauer, T., and Klebe, G. (1996) A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261(3), 470–489.

    Article  CAS  PubMed  Google Scholar 

  72. Morris, G. M., Goodsell, D. S., Halliday, R. S., et al. (1998) Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J. Comput. Chem. 19, 1639–1662.

    Article  CAS  Google Scholar 

  73. Morris, G. M., Goodsell, D. S., Huey, R., and Olson, A. J. (1996) Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J. Comput. Aided Mol. Des. 10, 293–304.

    Article  CAS  PubMed  Google Scholar 

  74. Jones, G., Willett, P., and Glen, R. C. (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of solvation. J. Mol. Biol. 245, 43–53.

    Article  CAS  PubMed  Google Scholar 

  75. Jones, G., Willett, P., Glen, R. C., Leach, A. R., and Taylor, R. (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748.

    Article  CAS  PubMed  Google Scholar 

  76. Schnecke, V. and Kuhn, L. A. (2000) Virtual screening with solvation and ligandinduced complimentarity. Perspect. Drug Discov. Des. 20, 171–190.

    Article  CAS  Google Scholar 

  77. McGann, M. R., Almond, H. R., Nicholls, A., and Brown, F. K. (2003) Gaussian docking functions. Biopolymers 68, 76–90.

    Article  CAS  PubMed  Google Scholar 

  78. Muegge, I. and Martin, Y. C. (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem. 42, 791–804.

    Article  CAS  PubMed  Google Scholar 

  79. Gelhaar, D. K., Verkhivker, G. M., Rejto, P. A., Sherman, C. J., Fogel, L. J., and Freer, S. T. (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem. Biol. 2, 317–324.

    Article  Google Scholar 

  80. Charifson, P. S., Corkery, J. J., Murcko, M. A., and Walters, W. P. (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 42, 5100–5109.

    Article  CAS  PubMed  Google Scholar 

  81. Böhm, H.-J. and Stahl, M. (1999) Rapid empirical scoring functions in virtual screening applications. Med. Chem. Res. 9, 445–462.

    Google Scholar 

  82. Muegge, I., Martin, Y. C., Hajduk, P. J., and Fesik, S. W. (1999) Evaluation of PMF scoring in docking weak ligands into the FK506 binding protein. J. Med. Chem. 42, 2498–2503.

    Article  CAS  PubMed  Google Scholar 

  83. Sippl, M. J., Ortner, M., Jaritz, M., Lackner, P., and Flöckner, H. (1996) Helmholtz free energies of atom pair interactions in proteins. Folding Des. 1, 289–298.

    Article  CAS  Google Scholar 

  84. Pearlman, D. A. and Charifson, P. S. (2001) Improved scoring of ligand-protein interactions using OWFEG free energy grids. J. Med. Chem. 44, 502–511.

    Article  CAS  PubMed  Google Scholar 

  85. Pearlman, D. A. and Charifson, P. S. (2001) Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system. J. Med. Chem. 44, 3417–3423.

    Article  CAS  PubMed  Google Scholar 

  86. Kellogg, G. E. and Abraham, D. J. (2000) Hydrophobicity: Is LogPo/w more than the sum of its parts? Eur. J. Med. Chem. 35, 651–661.

    Article  CAS  Google Scholar 

  87. Fornabaio, M., Spyrakis, F., Mozzarelli, A., Cozzini, P., Abraham, D. J., and Kellogg, G. E. (2004) Simple, intuitive, calculations of free energy of binding for protein-ligand complexes. 3. Including the free energy contribution of structural water molecules in HIV-1 protease-ligand complexes. J. Med. Chem. 47, 4507–4516.

    Article  CAS  PubMed  Google Scholar 

  88. Gussio, R., Zaharevitz, D W., McGrath, C. F., et al. (2000) Structure-based design modifications of the Paullone molecular scaffold for cyclin-dependent kinase inhibition. Anti-Cancer Drug Des. 15, 53–66.

    CAS  Google Scholar 

  89. Evers, A. and Klebe, G. (2004) Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model. J. Med. Chem. 47, 5381–5392.

    Article  CAS  PubMed  Google Scholar 

  90. Fornabaio, M., Rastinejad, F., Kharalkar, S., Safo, M., Abraham, D. J., and Kellogg, G. E. (2005) Virtual screening approach: application of a hydropathic forcefield to 3D database searches. In: 229th ACS National Meeting March 13–17, San Diego, CA.

    Google Scholar 

  91. Hurst, T. (1994) Flexible 3D searching: The directed tweak technique. J. Chem. Inf. Comput. Sci. 34, 190–196.

    CAS  Google Scholar 

  92. Needleman, S. B. and Wunsch, C. D. (1970) A general method applicacable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453.

    Article  CAS  PubMed  Google Scholar 

  93. Lipman, D. J. and Pearson, W. R. (1985) Rapid and sensitive protein similarity searches. Science 227, 1435–1441.

    Article  CAS  PubMed  Google Scholar 

  94. Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990) Basic local alignment search tool. J. Mol. Biol. 215, 403–410.

    CAS  PubMed  Google Scholar 

  95. Thompson, J. D., Higgins, D G., and Gibson, T. J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weightning, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680.

    Article  CAS  PubMed  Google Scholar 

  96. Canutescu, A. A., Shelenkov, A A., and Dunbrack, R. L. Jr. (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 12, 2001–2014.

    Article  CAS  PubMed  Google Scholar 

  97. Dunbrack, R. L. Jr. and Cohen, F. E. (1997) Bayesian statistical analysis of protein side-chain Rotamer preferences. Protein Sci. 6, 1661–1681.

    Article  CAS  PubMed  Google Scholar 

  98. Dunbrack, R. L. Jr. and Karplus, M. (1993) Backbone-dependent Rotamer library for proteins. J. Mol. Biol. 230, 543–574.

    Article  CAS  PubMed  Google Scholar 

  99. Morris, A. L., MacArthur, M. W., Hutchinson, E. G., and Thornton, J. M. (1992) Stereochemical quality of protein structure coordinates. Proteins 12, 345–364.

    Article  CAS  PubMed  Google Scholar 

  100. Vriend, G. (1990) WHAT IF: a molecular modeling and drug design program. J Mol. Graph. 8, 52–56.

    Article  CAS  PubMed  Google Scholar 

  101. Šali, A. and Blundell, T. L. (1993) Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Humana Press Inc., Totowa, NJ

About this chapter

Cite this chapter

Mosier, P.D., Kellogg, G.E. (2008). Molecular Modeling: Considerations for the Design of Pharmaceuticals and Biopharmaceuticals. In: Wu-Pong, S., Rojanasakul, Y. (eds) Biopharmaceutical Drug Design and Development. Humana Press. https://doi.org/10.1007/978-1-59745-532-9_13

Download citation

Publish with us

Policies and ethics