Skip to main content

Applications of Artificial Intelligence in Molecular Modelling and Drug Design

  • Chapter
Molecular Modelling and Drug Design

Part of the book series: Topics in Molecular and Structural Biology ((TMSB))

  • 48 Accesses

Abstract

Artificial intelligence (usually abbreviated to AI) is an area of research which has attracted much interest and controversy during its relatively short existence. Its origins are often traced to 1950, when Alan Turing published ‘Computing machinery and intelligence’.1 In the article, Turing raised the question ‘can machines think?’ Since that time, there has been considerable debate on the philosophical issues which such a question poses.2 This is not, however, the main focus of this chapter. Rather, I hope to illustrate some of the techniques and algorithms which have been developed by workers in AI that may prove useful in some of the problems encountered in molecular modelling.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 49, 433

    Article  Google Scholar 

  2. See, for example, (a) Penrose, R. (1989) The Emperor’s New Mind, Oxford University Press, Oxford;

    Google Scholar 

  3. Searle, J. (1987) Minds and brains without programs. In Mindwaves, ed. Blakemore, C. and Greenfield, S. A., Oxford University Press, Oxford, pp. 209–233;

    Google Scholar 

  4. Gregory, R. (1987) In defence of artificial intelligence — a reply to John Searle. In Mindwaves, ed Blakemore, C. and Greenfield, S. A., Oxford University Press, Oxford, pp. 235–244; (d) Collins, H. (1992) Will machines ever think?, New Scientist, 20 June, 36–40

    Google Scholar 

  5. There are a great number of introductory and advanced volumes which describe the techniques and applications of AI. This is a very limited selection of the texts in this field: (a) Barr, A. and Feigenbaum, E. A. (Eds)(1981) The Handbook of Artificial Intelligence (3 vols), William Kaufmann, Los Altos, Cal.;

    Google Scholar 

  6. Nilsson, N. J. (1982) Principles of Artificial Intelligence, Springer-Verlag, Berlin; (c) Collins, N. L. and Michie, D. (Eds), Machine Intelligence, Vols 1–10, Oliver and Boyd, Edinburgh;

    Book  Google Scholar 

  7. Bonnet, A. (1985) Artificial Intelligence: Promise and Performance, Prentice-Hall, London;

    Google Scholar 

  8. Winston, P. H. (1992) Artificial Intelligence, 3rd edn, Addison-Wesley, Reading, Mass.

    Google Scholar 

  9. Corey, E. J. (1991) The logic of chemical synthesis — multistep synthesis of complex carbogenic molecules, Angew. Chem. Int. Ed. Engl., 30, 455–465;

    Article  Google Scholar 

  10. Warren, S. A. (1982) Organic Synthesis: The Disconnection Approach, Wiley

    Google Scholar 

  11. Winston, P. H. (1992) Artificial Intelligence, 3rd edn, Addison-Wesley, Reading, Mass., pp. 63–100;

    Google Scholar 

  12. Barr, A. and Feigenbaum, E. A. (Eds)(1981) The Handbook of Artificial Intelligence, Vol. 1, William Kaufmann, Los Altos, Cal., Chapter 2;

    Google Scholar 

  13. Sedgewick, R. (1990) Algorithms in C, Addison-Wesley, Reading, Mass.

    Google Scholar 

  14. Hart, P. E., Nilsson, N. J. and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. SSC, 4, 100–104

    Google Scholar 

  15. Koschmann, T., Snyder, J. P., Johnson, P., Grace, T. and Evens, M. W. (1989). Conformational analysis using a truth maintenance system, J. Mol. Graph., 6, 74–79

    Article  Google Scholar 

  16. As with texts on Artificial Intelligence, there are a large number of books and papers concerned with Expert systems. A limited selection follows: (a) Smith, D. H. Artificial Intelligence: The technology of Expert systems. In Artificial Intelligence Applications in Chemistry, ACS Symposium Series 306, pp. 1–16; (b) Hayes-Roth, F., Waterman, P. A. and Lenat, D. (1983) Building Expert Systems, Addison-Wesley, Reading, Mass.;

    Google Scholar 

  17. Waterman, D. A. (1986) A Guide to Expert Systems, Addison-Wesley, Reading, Mass.;

    Google Scholar 

  18. Sell, P. S. (1985) Expert Systems: A Practical Introduction, Macmillan, London

    Google Scholar 

  19. Feigenbaum, E. A. (1977). The art of artificial intelligence: Themes and case studies of knowledge engineering, Int. Joint. Conf. on AI, Vol. 5, pp. 1014–1029

    Google Scholar 

  20. Sinclair, A. (1951) The Traditional Formal Logic, Methuen, London;

    Google Scholar 

  21. Mitchell, D. (1962) An Introduction To Logic, Hutchinson University Library, London

    Google Scholar 

  22. Clocksin, W. F. and Mellish, C. S. (1981). Programming in Prolog, Springer-Verlag, Berlin

    Google Scholar 

  23. Gray, N. A. B. (1988). Artificial Intelligence in chemistry, Anal. Chim. Acta, 210, 9–32

    Article  Google Scholar 

  24. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A. and Lederberg, J. (1980). The DENDRAL Project, McGraw-Hill, New York

    Google Scholar 

  25. Corey, E. J. and Wipke, W. T. (1969) Computer-assisted design of complex organic syntheses, Science, 166, 178;

    Article  Google Scholar 

  26. Corey, E. J., Long, A. K. and Rubenstein, S. D. (1985) Computer-assisted analysis in organic synthesis, Science, 228, 408–418

    Article  Google Scholar 

  27. Pensak, D. A. and Corey, E. J. (1977). LHASA — logic and heuristics applied to synthetic analysis. In ACS Symposium Series 61, p. 1

    Google Scholar 

  28. Wipke, W. T., Braun, H., Smith, G., Choplin, F. and Seiber, W. (1977). SECS — simulation and evaluation of chemical synthesis: strategy and planning. In ACS Symposium Series 61, pp. 97–127

    Google Scholar 

  29. Gerlenter, H., Sridharan, N. S., Hart, A. J. and Yen, S.-C. (1973) The discovery of organic synthetic routes by computer, Topics Curr. Chem., 41, 113;

    Google Scholar 

  30. Gerlenter, H., Saunders, A. F., Larsen, D. L., Argarwal, K. K., Boivie, R. H., Spritzer, G. A. and Searleman, J. E. (1977) Empirical exploration of SYNCHEM, Science, 197, 1041–1049

    Article  Google Scholar 

  31. Howard, A. E. and Kollman, P. A. (1988) An analysis of current methodologies for conformational searching of complex molecules, J. Med. Chem., 31, 1669–1675;

    Article  Google Scholar 

  32. Leach, A. R. (1991) A survey of methods for searching the conformational space of small and medium-sized molecules, in Reviews in Computational Chemistry, Vol. 2, ed. Lipkowitz, K. B. and Boyd, D. B., VCH Publishers pp. 1–54

    Chapter  Google Scholar 

  33. Lipton, M. and Still, W. C. (1988). The multiple minimum problem in molecular modeling. Tree searching internal coordinate conformational space, J. Comp. Chem., 9, 343–355

    Article  Google Scholar 

  34. Motoc, I., Dammkoehler, R. A., Mayer, D. and Labanowski, J. (1986) Three-dimensional quantitative structure-activity relationships. I. General approach to the pharmacophore model validation, Quant. Struct. -Act. Rel., 5, 99–105;

    Article  Google Scholar 

  35. Motoc, I., Dammkoehler, R. A. and Marshall, G. R. (1986) Three-dimensional structure-activity relationships and biological receptor mapping, in Mathematical and Computational Concepts in Chemistry, ed. Trinajstic, N., Ellis Horwood, Chichester, pp. 222–257;

    Google Scholar 

  36. Dammkoehler, R. A., Karasek, S. F., Shands, E. F. B. and Marshall, G. R. (1989) Constrained search of conformational hypersurface, J. Comp.-Aided Mol. Des., 3, 3–21

    Article  Google Scholar 

  37. Dolata D. P. and Carter R. E. (1987). WIZARD: Applications of expert systems techniques to conformational analysis. 1. The basic algorithms exemplified on simple hydrocarbons, J. Chem. Inf. Comp. Sci., 27, 36–47

    Article  Google Scholar 

  38. Dolata, D. P., Leach, A. R. and Prout, K. (1987) WIZARD: AI in conformational analysis, J. Comp.-Aided Mol. Des., 1, 73–85;

    Article  Google Scholar 

  39. Dolata, D. P., Leach, A. R. and Prout, K. (1989) Molecular modelling by symbolic logic, in Computer-Aided Molecular Design, ed. Richards, W. G., IBC Technical Services, pp. 67–82

    Google Scholar 

  40. Leach, A. R. and Prout, K. (1990) Automated conformational analysis: Directed conformational search using the A* algorithm, J. Comp. Chem., 11, 1193–1205;

    Article  Google Scholar 

  41. Leach, A. R. (1991) Automated conformational analysis and search, Pest. Sci., 33, 87–96

    Article  Google Scholar 

  42. Leach, A. R., Dolata, D. P. and Prout, K. (1990). Algorithms for the analysis of molecular structure, J. Chem. Inf. Comp. Sci., 30, 316–324

    Article  Google Scholar 

  43. Leach, A. R., Prout, K. and Dolata, D. P. (1988). An investigation into the construction of molecular models by the template joining method , J. Comp.-Aided Mol. Des., 2, 107–123

    Article  Google Scholar 

  44. Leach, A. R., Prout, K. and Dolata, D. P. (1990). The application of Artificial Intelligence to the conformational analysis of strained molecules, J. Comp. Chem., 11, 680–693

    Article  Google Scholar 

  45. Leach, A. R., Prout, K. and Dolata, D. P. (1990). Automated conformational analysis: algorithms for the efficient construction of low-energy conformations, J. Comp.-Aided Mol Des., 4, 271–283

    Article  Google Scholar 

  46. 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  Google Scholar 

  47. Leach, A. R. (1989). The Application of Artificial Intelligence Techniques in Conformational Analysis, D. Phil. thesis, Oxford University

    Google Scholar 

  48. Leach, A. R. and Smellie, A. S. (1992). A combined model-building and distance geometry approach to automated conformational analysis, J. Chem. Inf. Comp. Sci., 32, 379–385

    Article  Google Scholar 

  49. 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  Google Scholar 

  50. DesJarlais, R. L., Sheridan, R. P., Seibel, G. L., Dixon, J. S. and Kuntz, I. D. (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

    Article  Google Scholar 

  51. The Fine Chemicals Directory is distributed by Molecular Design Ltd, 2132 Farallon Drive, San Leandro, California 94577

    Google Scholar 

  52. Corey, E. J. and Bailar, J. C. (1959). The stereochemistry of complex inorganic compounds. XXII. Stereospecific effects in complex ions, J. Am. Chem. Soc, 81, 2620–2629

    Article  Google Scholar 

  53. Leach, A. R. (1993). Constitutional, configurational and conformational analysis of transition metal coordination complexes, J. Comp. Aided, Mol. Des., 7, 225–240

    Article  Google Scholar 

  54. Clark, D. A., Barton, G. J. and Rawlings, C. J. (1990) A knowledge-based architecture for protein sequence analysis and structure prediction, J. Mol. Graph, 8, 94–107;

    Article  Google Scholar 

  55. Rawlings, C. J. (1989) Databases, Artificial Intelligence and knowledge-based systems for molecular biology, Biochem. Soc. Trans., 17, 851–858

    Article  Google Scholar 

  56. Klopman, G. (1984) Artificial Intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules, J. Am. Chem. Soc, 106, 7315–7321;

    Article  Google Scholar 

  57. Klopman, G. and Marina, O. T. (1987) Computer-automated structure evaluation of antileukemic 9-aminoacridines, Mol. Pharmacol., 31, 457–476;

    Google Scholar 

  58. Klopman, G. and Cimayuga, M. L. (1988) Computer-automated structure evaluation of flavonoids and other structurally related compounds as glyoxalase 1 enzyme inhibitors, Mol. Pharmacol., 34, 218–222;

    Google Scholar 

  59. Klopman, G. and Buyukbingol, E. (1988) An Artificial Intelligence approach to the study of the structural moieties relevant to drug-receptor interactions in aldose reductase inhibitors, Mol. Pharmacol., 34, 852–862;

    Google Scholar 

  60. Rosenkranz, H. S. and Klopman, G. (1990) ‘Cryptic’ mutagens and carcinogenicity, Mutagenesis, 5, 199–202;

    Article  Google Scholar 

  61. Rosenkranz, H. S. and Klopman, G. (1990) Structural alerts to genotoxicity: the interaction of human and artificial intelligence, Mutagenesis, 5, 333–361;

    Article  Google Scholar 

  62. Klopman, G. and Dimayuga, M. L. (1990) Computer-automated structure evaluation (CASE) of the teratogenicity of reinoids with the aid of a novel geometry index, J. Comp.-Aided Mol. Des., 4, 117–130;

    Article  Google Scholar 

  63. Rosenkranz, H. S. and Klopman, G. (1990) New structural concepts for predicting carcinogenicity in rodents: An Artificial Intelligence approach, Teratogen., Carcinogen. Mutagen., 10, 73–88

    Article  Google Scholar 

  64. Bolis, G., Di Pace, L. and Fabrocini, F. (1991). A machine learning approach to computer-aided molecular design, J. Comp.-Aided Mol. Des., 5, 617–628

    Article  Google Scholar 

  65. Klein, T. E., Huang, C, Ferrin, T. E., Langridge, R. and Hansch, C. Computer-assisted drug receptor mapping analysis, in Artificial Intelligence Applications in Chemistry, ACS Symposium Series 306, pp. 147–158

    Google Scholar 

  66. Darvas, F. (1988) Predicting metabolic pathways by logic programming, J. Mol. Graph., 6, 80–86;

    Article  Google Scholar 

  67. Valko, K., Szabo, G., Rohricht, J., Jemnitz, K. and Darvas, F. (1989) Prediction of retention of metabolites in high-performance liquid chromatography by an expert system approach, J. Chroma-tog., 485, 349–363;

    Article  Google Scholar 

  68. Kalasz, H., Bathori, M., Tarjanyi, Z. and Darvas, F. (1990) Computer simulation of ecdysone metabolism and of the HPLC analysis of the metabolites, Chromatographic, 30, 95–98

    Article  Google Scholar 

  69. Testa, B. and Jenner, P. (1976). Drug Metabolism: Chemical and Biochemical Aspects, Marcel Dekker, New York

    Google Scholar 

  70. Muggleton, S. and Feng, C. (1990). In Proc. 1st Conf. on Algorithmic Learning Theory, ed. Arikawa, S., Goto, S. Ohsuga, S. and Japanese Society for Artificial Intelligence, Tokyo, pp. 368–381

    Google Scholar 

  71. Sternberg, M. J. E., Lewis, R. A., King, R. D. and Muggleton, S. (1992). Modelling the structure and function of enzymes by machine learning, Faraday Discuss., 93, 269–280

    Article  Google Scholar 

  72. Quinlan, J. R. (1986). Induction of decision trees, Machine Learning, 1, 81–106

    Google Scholar 

  73. A-Razzak, M. and Glen, R. C. (1992) Applications of rule induction in the derivation of quantitative structure activity relationships, J. Comp.-Aided Mol. Des., 6, 349–383

    Article  Google Scholar 

  74. Jones, D. S. (1979). Elementary Information Theory, Clarendon Press, Oxford

    Google Scholar 

  75. Beale R. and Jackson, T. (1990) Neural Computing: An Introduction, Adam Hilger;

    Book  Google Scholar 

  76. Sanchez-Sinencio, E. and Lau, C. (Eds.) (1992) Artificial Neural Networks. Paradigms, Applications and Hardware Implementations, IEEE Press

    Google Scholar 

  77. Minsky, M. and Papert, S. (1969). Perceptrons, MIT Press, Cambridge, Mass.

    Google Scholar 

  78. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagating errors, Nature, 323, 533–536;

    Article  Google Scholar 

  79. McClelland, J. L. and Rumelhart, D. E. (1986) Parallel Distributed Processing, MIT Bradford Press, Cambridge, Mass.

    Google Scholar 

  80. Sejnowski, T. J. and Rosenberg, C. R. (1987). Parallel networks that learn to pronounce English text, Complex Systems, 1, 145–168

    Google Scholar 

  81. Qian, N. and Sejnowski, T. J. (1988) Predicting the secondary structure of globular proteins using neural network models, J. Mol. Biol, 202, 865–881;

    Article  Google Scholar 

  82. Bohr, H., Bohr, J., Brunak, S., Cotterill, R. M., Lautrup, B., Norskov, L., Olsen, O. H. and Petersen, S. B. (1988) Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin, FEBS Lett., 241, 223–228;

    Article  Google Scholar 

  83. Holley, L. H. and Karplus, M. (1989) Protein secondary structure prediction with a neural network, Proc. Natl Acad. Sci. USA, 86, 152–156;

    Article  Google Scholar 

  84. Kneller, D. G., Cohen, F. E. and Langridge, R. (1990) Improvements in protein secondary structure prediction by an enhanced neural network, J. Mol. Biol., 214, 171–182;

    Article  Google Scholar 

  85. Stolorz, P., Lapedes, A. and Xia, Y. (1992) Predicting protein secondary structure using neural net and statistical methods, J. Mol. Biol., 225, 363–377

    Article  Google Scholar 

  86. Aoyama, T., Suzuki, Y. and Ichikawa, H. (1990) Neural networks applied to structure-activity relationships, J. Med. Chem., 33, 905–908;

    Article  Google Scholar 

  87. Aoyama, T., Suzuki, Y. and Ichikawa, H. (1990) Neural networks applied to quantitative structure-activity relationship analysis, J. Med. Chem., 33, 2583–2590;

    Article  Google Scholar 

  88. Andrea, T. A. and Kalayeh, H. (1991) Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors, J. Med. Chem., 34, 2824–2836

    Article  Google Scholar 

  89. Gillet, V. J., Flanagan, K., Johnson, P. A., Marshall, C, Mata, P. and Sike, S. (1991). Automated Structure Design in 3D, Abs. Pap. ACS 202, p. 48

    Google Scholar 

  90. Lewis, R. A. and Dean, P. M. (1989) Automated site-directed drug design: the concept of space skeletons in primary structure generation, Proc. R. Soc. Lond., B236, 125–140;

    Article  Google Scholar 

  91. Lewis, R. A. and Dean, P. M. (1989) Automated site-directed drug design: the formation of molecular templates in primary structure generation, Proc. R. Soc. Lond., B236, 141–162

    Article  Google Scholar 

  92. Danzinger, D. J. and Dean, P. M. (1989) Automated site-directed drug design: a general algorithm of knowledge acquisition about hydrogen-bonding regions at protein surfaces, Proc. R. Soc. Lond., B236, 101–113;

    Article  Google Scholar 

  93. Danzinger, D. J. and Dean, P. M. (1989) Automated site-directed drug design: the prediction and observation of ligand point positions at hydrogen-bonding regions on protein surfaces, Proc. R. Soc. Lond., B236, 115–124

    Article  Google Scholar 

  94. Leach, A. R. and Kuntz, I. D. (1992). The conformational analysis of flexible molecules in macromolecular receptor sites, J. Comp. Chem., 13, 730–748

    Article  Google Scholar 

Download references

Authors

Editor information

J. G. Vinter Mark Gardner

Copyright information

© 1994 J. G. Vinter and M. Gardner

About this chapter

Cite this chapter

Leach, A.R. (1994). Applications of Artificial Intelligence in Molecular Modelling and Drug Design. In: Vinter, J.G., Gardner, M. (eds) Molecular Modelling and Drug Design. Topics in Molecular and Structural Biology. Palgrave, London. https://doi.org/10.1007/978-1-349-12973-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-349-12973-7_6

  • Publisher Name: Palgrave, London

  • Print ISBN: 978-1-349-12975-1

  • Online ISBN: 978-1-349-12973-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics