Skip to main content

Reducing the Cost of Evaluation of the Gradient and Hessian of Molecular Potential Energy Functions

  • Conference paper
Frontiers in Global Optimization

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 74))

  • 869 Accesses

Abstract

One of the important points in molecular conformation problems is the evaluation of the gradient and Hessian of potential energy functions. These functions consist of a set of energy contributions, called the force field, for approximating the interactions between atoms of a molecule. Our discussion will focus on potential energy functions that have most common terms in general force fields. We can describe potential energy functions using Cartesian coordinates or internal coordinates (bond lengths, bond angles, and torsion angles). Analytic evaluation of the gradient with respect to the internal coordinates requires O(N 4) steps, where N is the number of atoms involved. We prove that the gradient and Hessian with respect to the Cartesian coordinates are evaluated in O(N 2)and O(N 3)steps, respectively.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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.

References

  1. Allinger N. L., Yuh Y. H. and Lii J.-H. (1989), “Molecular mechanics. The MM3 force field for hydrocarbons”, J. Am. Chem. Soc., Vol. 111, 8551–8582.

    Article  Google Scholar 

  2. Barbosa H., Raupp F., Lavor C., Lima H. and Maculan N. (2000), “A hybrid genetic algorithm for finding stable conformations of small molecules”, Proc. of the VIth Brazilian Symposium on Neural Networks, IEEE Computer Society Press, Los Alamitos, 90–94.

    Google Scholar 

  3. Brodmeier T. and Pretsch E. (1994), “Application of genetic algorithms in molecular modeling”, J. Comp. Chem., Vol. 15, 588–595.

    Article  Google Scholar 

  4. Brooks B. R., Bruccoleri R. E., Olafson B. D., States D. J., Swaminathan S. and Karplus M. (1983), “CHARMM: a program for macromolecular energy minimization and dynamics calculations”, J. Comp. Chem. Vol. 4, 187–217.

    Article  Google Scholar 

  5. Floudas C. A., Klepeis J. L. and Pardalos P. M. (1999), “Global Optimization Approaches in Protein Folding and Peptide Docking,” In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 47, 141–171, American Mathematical Society, Providence, Rhode Island.

    Google Scholar 

  6. Floudas C. A. and Pardalos P. M. (2000), Optimization in Computational Chemistry and Molecular Biology, Nonconvex Optimization and its Applications, Vol. 40, Kluwer Academic Publishers, The Netherlands.

    MATH  Google Scholar 

  7. Kawai H., Kikuchi T. and Okamoto Y. (1989), “A prediction of tertiary structures of peptide by Monte Carlo simulated annealing method”, Protein Eng., Vol. 3, 85–94.

    Article  Google Scholar 

  8. Kostrowicki J. and Scheraga H. A. (1992), “Application of the diffusion equation method for global optimization to oligopeptides”, J. Phys. Chem., Vol. 96, 7442–7449.

    Article  Google Scholar 

  9. Maranas C. D. and Floudas C. A. (1994), “Global Minimum Potential Energy Conformations of Small Molecules”, J. Global Opt., Vol. 4, 135–170.

    Article  MathSciNet  MATH  Google Scholar 

  10. Maranas C. D. and Floudas C. A. (1994), “A Deterministic Global Optimization Approach for Molecular Structure Determination”, J. Chem. Phys., Vol. 100, 1247–1261.

    Article  Google Scholar 

  11. Moret M. A., Pascutti P. G., Bisch P. M. and Mundim K. C. (1998), “Stochastic molecular optimization using generalized simulated annealing”, J. Comp. Chem, Vol. 19, 647–657.

    Article  Google Scholar 

  12. Némethy G., Gibson K. D., Palmer K. A., Yoon C. N., Paterlini G., Zagari A., Rumsey S. and Scheraga H. A. (1992), “Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm with application to proline-containing peptides”, J. Phys. Chem. Vol. 96, 6472–6484.

    Article  Google Scholar 

  13. Neumaier A. (1997), “Molecular Modeling of Proteins and Mathematical Prediction of Protein Structure,” SIAM Rev., Vol. 39, 407–460.

    Article  MathSciNet  MATH  Google Scholar 

  14. Pardalos P. M., Shalloway D. and Xue G. L. (1994), “Optimization methods for computing global minima of nonconvex potential energy functions”, J. Global Optim., Vol. 4, 117–133.

    Article  MathSciNet  MATH  Google Scholar 

  15. Phillips A. T., Rosen J. B. and Walke V. H. (1996), “Molecular structure determination by convex underestimation of local energy minima”, In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 23, 181–198, American Mathematical Society, Providence, Rhode Island.

    Google Scholar 

  16. Piela L., Kostrowicki J. and Scheraga H. A. (1989), “The multiple-minima problem in the conformational analysis of molecules. Deformation of the potential energy hyper-surface by the diffusion equation method”, J. Phys. Chem., Vol. 93, 3339–3346.

    Article  Google Scholar 

  17. Pogorelov A. (1987), Geometry, Mir Publishers, Moscow.

    Google Scholar 

  18. Thompson H. B. (1967), “Calculation of Cartesian Coordinates and their Derivatives from Internal Molecular Coordinates”, J. Chem. Phys., Vol. 47, 3407–3410.

    Article  Google Scholar 

  19. Troyer J. M. and Cohen F. E. (1991),“Simplified models for understanding and predicting protein structure”, Reviews in Computational Chemistry, Vol. II, 57–80, VCH Publ., New York.

    Article  Google Scholar 

  20. Wales D. J. and Scheraga H. A. (1999), “Global optimization of clusters, crystals and biomolecules”, Science, Vol. 285, 1368–1372.

    Article  Google Scholar 

  21. Weiner S. J., Kollmann P. A., Nguyen D. T. and Case D. A. (1986), “An all atom force field for simulations of proteins and nucleic acids”, J. Comp. Chem., Vol. 7, 230–252.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Kluwer Academic Publishers

About this paper

Cite this paper

Lavor, C., Maculan, N. (2004). Reducing the Cost of Evaluation of the Gradient and Hessian of Molecular Potential Energy Functions. In: Floudas, C.A., Pardalos, P. (eds) Frontiers in Global Optimization. Nonconvex Optimization and Its Applications, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0251-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-0251-3_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7961-4

  • Online ISBN: 978-1-4613-0251-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics