Skip to main content

The Developer’s Perspective — What Can Be Achieved in Hardware and Software

  • Chapter
  • 42 Accesses

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

Abstract

The remarkable growth of computational chemistry over the past two decades or so, in both industrial and academic laboratories, is (it is to be hoped) a testimony to the fact that it can provide useful results; thus, I shall treat the question ‘Why should we bother with computational chemistry?’ as a solved problem. However, having attained a position of reasonable stability (if not quite respectability) in many areas of chemistry, the time is ripe for computational chemists to look critically at the methods which are in common use, and consider where they can be improved to give better results and to allow their extension into fields where computational chemistry currently has few uses.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. van’t Hoff, J. T. (1874). Sur les formules de structure dans l’espace, Arch. Neerl. Sci., 9, 445–454

    Google Scholar 

  2. le Bel, J.-A. (1874). Sur les relations qui existent entre les formules atomiques des corps organiques et le pouvoir rotatoire de leurs dissolutions, Bull. Soc. Chim. France, 22, 337–347

    Google Scholar 

  3. Westheimer, F. H. (1956). Calculation of the magnitude of steric effects. In Steric Effects in Organic Chemistry, ed. Newman, M. S., Wiley, New York, p. 523

    Google Scholar 

  4. Carbo, R., Molino, L., Calabuig, B. (1992). A concurrent algorithm for parallel calculation of eigenvalues and eigenvectors of real symmetric matrices, J. Comp. Chem., 13, 155–159

    Article  Google Scholar 

  5. Luthi, H. P., Mertz, J. E., Feyereisen, M. W., Almof, J. E. (1992). A coarse-grain parallel implementation of the direct SCF method, J. Comp. Chem., 13, 160–164

    Article  Google Scholar 

  6. Pople, J. A., Beveridge, D. L. (1971). Approximate Molecular Orbital Theory, Wiley, New York

    Google Scholar 

  7. Stewart, J. J. P. (1990). MOPAC: A semiempirical molecular orbital programme, J. Comp.-Aided Mol. Des., 4, 1–105

    Article  Google Scholar 

  8. Murrell, J. N., Kettle, S. F. A., Tedder, J. M. (1985). The Chemical Bond, 2nd edn, Wiley, Chichester, Ch. 13

    Google Scholar 

  9. Parr, R. G., Yang, W. (1989). Density Functional Theory of Atoms and Molecules, Oxford University Press, New York

    Google Scholar 

  10. Andzelm, J., Wimmer, E. (1992). Density functional Gaussian-type-orbital approach to molecular geometries, vibrations, and reaction energies, J. Chem. Phys., 96, 1280–1303

    Article  Google Scholar 

  11. Clementi, E., Chakravorty, S. J., Corongiu, G., Sonnad, V. (1991). Independent electron models: Hartree-Fock for many-electron atoms. In MOTECC 1990, ed. Clementi, E., ESCOM, Leiden, p. 119

    Google Scholar 

  12. Klopman, G. (1967). Solvations: A semi-empirical procedure for including solvation in quantum mechanical calculations of large molecules, Chem. Phys. Lett., 1, 200–202

    Article  Google Scholar 

  13. Kollmann, P., Kuntz, I. (1976). Hydration of NH4F, J. Am. Chem. Soc., 98, 6820

    Article  Google Scholar 

  14. Born, M. (1920). Volumes and heats of hydration of ions, Z. Phys., 1, 45–48

    Article  Google Scholar 

  15. Onsager, L. (1936). Electric moments of molecules in liquids, J. Am. Chem. Soc., 58, 1486

    Article  Google Scholar 

  16. Kirkwood, J. G. (1939). The dielectric polarization of polar liquids, J. Chem. Phys., 7, 911

    Article  Google Scholar 

  17. Duben, A. J., Miertus, S. (1981). The effect of solvent on the internal rotation of formamide: A CNDO/2-solvation method study, Theoret. Chim. Acta, 60, 327–337

    Article  Google Scholar 

  18. Ando, I., Webb, G. A. (1981). Some quantum chemical aspects of solvent effects on NMR parameters, Org. Magn. Reson., 15, 111–130

    Article  Google Scholar 

  19. Cramer, C. J., Truhlar, D. G. (1991). General Parameterized SCF Model for Free Energies of Solvation in Aqueous Solution, University of Minnesota Supercomputer Institute Research Report UMSI 91/177, July

    Google Scholar 

  20. Saunders, M. R., Webb, G. A., Tute, M. S. (1987). A theoretical study of solvent effects on molecular electronic properties, J. Mol. Struct., 158, 69–78

    Article  Google Scholar 

  21. Cramer, C. J., Truhlar, D. G. AMSOL, QCPE 606

    Google Scholar 

  22. GAUSSIAN 92: Gaussian Inc., 4415 Fifth Avenue, Pittsburgh, PA15213, USA

    Google Scholar 

  23. Gillespie, R. J., Nyholm, R. S. (1957). Inorganic stereochemistry, Q. Rev. (London), 11, 339–380

    Article  Google Scholar 

  24. Gillespie, R. J. (1992). Electron densities and the VSEPR model of molecular geometry, Can. J. Chem., 70, 742–750

    Article  Google Scholar 

  25. Bartell, L. S., Bershad, Y. Z. (1984). Valence-shell electron-pair repulsions: a quantum test of a naive mechanical model, J. Am. Chem. Soc., 106, 7700–7703

    Article  Google Scholar 

  26. Ermer, O., Lifson, S. (1973). Consistent force-field calculations. III. Vibrations, conformations and heats of hydrogenation of non-conjugated olefins, J. Am. Chem. Soc., 95, 4121–4132

    Article  Google Scholar 

  27. MM2MX QCPE 588, Quantum Chemistry Programme Exchange, Creative Arts Building 181, Indiana University, Bloomington, IN 47405, USA

    Google Scholar 

  28. Ferguson, D. M., Raber, D. J. (1990). Molecular mechanics calculations of several lanthanide complexes: an application of the Random Incremental Pulse Search, J. Comp. Chem., 11, 1061–1071

    Article  Google Scholar 

  29. Hambley, T. W. (1988). Molecular mechanics analysis of the influence of inter-ligand interactions on isomer stabilities and barriers to isomer interconversion in diammine- and bis(amine)bis(purine)-platinum(II) complexes, Inorg. Chem., 21, 1073

    Article  Google Scholar 

  30. Comba, P. (1989). Coordination geometries of hexamine cage complexes, Inorg. Chem., 28, 426

    Article  Google Scholar 

  31. Deeth, R. J., private communication

    Google Scholar 

  32. Sanders, J. K. M., Hunter, C. A. (1990). The nature of π−π interactions, J. Am. Chem. Soc., 112, 5525–5534

    Article  Google Scholar 

  33. Vinter, J. G., private communication

    Google Scholar 

  34. Field, M. J., Bash, P. A., Karplus, M. (1990). A combined quantum mechanical and molecular mechanical potential for molecular dynamics simulations, J. Comp. Chem., 11, 700–733

    Article  Google Scholar 

  35. Aqvist, J., Warshel, A. (1992). Computer simulation of the initial proton transfer step in human carbonic anhydrase I, J. Mol. Biol., 224, 7–14

    Article  Google Scholar 

  36. Topliss, J. G., Edwards, R. P. (1979). Chance effects in QSAR studies. In Computer Aided Drug Design, ed. Olsen, E. C. and Christofferson, R. E., ACS Symposium Series No. 112, Washington, D.C., p. 131

    Google Scholar 

  37. Wells, P. R. (1968). Linear Free Energy Relationships, Academic Press, New York

    Google Scholar 

  38. Anscombe, F. J. (1973). Graphs in statistical analysis, American Statistician, 27, 17–21

    Google Scholar 

  39. Efron, B., Tibshirani, R. (1991). Statistical data analysis in the computer age, Science, 253, 390–395

    Article  Google Scholar 

  40. Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T. (1986). Numerical Recipes. The Art of Scientific Computing, Cambridge University Press, Cambridge, Ch. 13.8

    Google Scholar 

  41. Broughton, H. B., Green, S. M., Rzepa, H. S. (1992). Rank correlation of AM1 and PM3 derived molecular electrostatic potentials (RACEL) with Hammett σp-parameters, J. Chem. Soc. Chem. Commun., 37–39

    Google Scholar 

  42. Kama, K. N., Breen, D. M. (1991). An artificial neural network tutorial: part 1 — basics, Neural Nets, 1, 4–23

    Google Scholar 

  43. ANSIM: Science Applications International Corporation, San Diego, California, USA

    Google Scholar 

  44. BIOPROP. S. M. Muskal, Laboratory of Biodynamics, University of California, Berkeley, CA 94709, USA

    Google Scholar 

  45. Veith, M., Kolinski, A. (1991). Prediction of protein secondary structure by an enhanced neural network, Acta Biochim. Polon., 38, 335–351

    Google Scholar 

  46. Livingstone, D. J., Hesketh, G., Clayworth, D. (1991). Novel method for the display of multivariate data using neural networks, J. Mol. Graph., 9, 115–118

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Andrea, T. A., 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 

  49. Ripley, B. D. (1993). Statistical aspects of neural networks. In Chaos and Networks — Statistical and Probabilistic Aspects, ed. Barndorff-Nielsen, O. E., Cox, D. R., Jensen, J. L. and Kendall, W. S., Chapman and Hall, London

    Google Scholar 

  50. Manallack, D. T., Livingstone, D. J. (1992). Artificial neural networks: application and chance effects for QSAR data analysis, Med. Chem. Res., 2, 181–190

    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

Saunders, M.R. (1994). The Developer’s Perspective — What Can Be Achieved in Hardware and Software. 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_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-349-12973-7_11

  • 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