Skip to main content
SpringerLink
Log in

Molecular Evolution: Automated Manipulation of Hierarchical Chemical Topology and Its Application to Average Molecular Structures

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

A simple hierarchical data structure (tree) and associated set of algorithms (written in Mathematica) have been developed that permit the direct manipulation of the topology of a molecule while simultaneously maintaining valid chemical valence. Coupled with a genetic algorithm optimization engine, these computational tools can be used to optimize chemical structures under the guidance of an appropriate fitness function. A detailed study of the factors that influence the performance of the method revealed that it is strongly dependent on the size and complexity of the evolved chemical structures. The effects of population size and choice of genetic operators are much smaller. The results of an exploration into the discovery of average molecular structures using this methodology is also described.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. P. W. Atkins, Molecules, Scientific American Library series #21, HPHL: New York, NY, 1987.

    Google Scholar 

  2. S. H. Bertz, “On the complexity of graphs and molecules,” Bull. Math. Biol. vol. 45 pp. 849-855, 1983.

    Google Scholar 

  3. S. Budavari, M. J. O'Neil, A. Smith, and P. E. Heckelman, The Merck Index, An Encyclopedia of Chemicals, Drugs, and Biologicals, 12th ed., Merck & Co., Inc.: Rahway, NJ, 1996.

    Google Scholar 

  4. R. E. Carhart, D. S. Smith, and R. Venkataraghavan, “Atom pairs as molecular features in structure-activity studies: Definitions and applications,” J. Chem. Inf. Comput. Sci. vol. 25 pp. 64-73, 1985.

    Google Scholar 

  5. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecol. vol. 26 pp. 297-302, 1945.

    Google Scholar 

  6. R. Gautzsch and P. Zinn, “List operations on chemical graphs. 1. Basic list structures and operations,” J. Chem. Inf. Comput. Sci. vol. 32 pp. 541-550, 1992.

    Google Scholar 

  7. F. E. Harrell, Predicting Outcomes: Applied Survival Analysis and Logistic Regression, University of Virginia Bookstore: Charlottesville, VA, 1998.

    Google Scholar 

  8. C. Jacob, “Evolving evolution programs: Genetic programming and L-systems,” Genetic Programming: Proceedings of the First Annual Conference, The MIT Press, Cambridge, MA, 1996, pp. 107-115.

    Google Scholar 

  9. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press: Cambridge, MA, 1992.

    Google Scholar 

  10. V. Kvasnicka and J. Pospichal, “Simulated annealing construction of molecular graphs with required properties,” J. Chem. Inf. Comput. Sci. vol. 36 pp. 516-526, 1996.

    Google Scholar 

  11. S. Luke and L. Spector, “Evolving graphs and networks with edge encoding: Preliminary report,” in Late Breaking Papers at the Genetic Programming 1996 Conference, Stanford, CA, 1996.

  12. Mathsoft, Inc., S-PLUS 4.5 for NT software and documentation, Mathsoft, Inc., Seattle, WA, 1997.

    Google Scholar 

  13. R. B. Nachbar, “Genetic programming,” Mathematica J. vol. 5 pp. 36-47, 1995.

    Google Scholar 

  14. R. B. Nachbar, “Molecular evolution: A hierarchical representation for chemical topology and its automated manipulation,” Proc. Third Ann. Gen. Prog. Conf., Madison, WI, 1998, pp. 246-253.

  15. J. J. Sepkoski, “Quantified coefficients of association and measurement of similarity,” Math. Geol. vol. 6 pp. 135-152, 1974.

  16. R. P. Sheridan and S. K. Kearsley, “Using a genetic algorithm to suggest combinatorial libraries,” J. Chem. Inf. Comput. Sci. vol. 35 pp. 310-320, 1995.

    Google Scholar 

  17. R. P. Sheridan, personal communication, 1999.

  18. V. Venkatasubramanian, K. Chan, and J. M. Caruthers, “Computer-aided molecular design using genetic algorithms,” Computers Chem. Enging. vol. 18 pp. 833-843, 1994.

    Google Scholar 

  19. V. Venkatasubramanian, K. Chan, and J. M. Caruthers, “Evolutionary design of molecules with desired properties using the genetic algorithm,” J. Chem. Inf. Comput. Sci. vol. 35 pp. 188-195, 1995.

    Google Scholar 

  20. L. Weber, S. Wallbaum, C. Broger, K. Gubernator, “Optimization of the biological activity of combinatorial compound libraries by a genetic algorithm,” Angew. Chem., Int. Ed. Eng. vol. 34 pp. 2280-2282, 1995.

    Google Scholar 

  21. D. Weininger, “SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules,” J. Chem. Inf. Comput. Sci. vol. 28 pp. 31-36, 1988.

    Google Scholar 

  22. S. Wolfram, The Mathematica Book, 3rd ed. Wolfram Media, Cambridge University Press: Cambridge, United Kingdom, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Merck Research Laboratories, Information Sciences Research Department, 126 E. Lincoln Ave., P.O. Box 2000, Rahway, NJ, 07065

    Robert B. Nachbar

Authors
  1. Robert B. Nachbar
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nachbar, R.B. Molecular Evolution: Automated Manipulation of Hierarchical Chemical Topology and Its Application to Average Molecular Structures. Genetic Programming and Evolvable Machines 1, 57–94 (2000). https://doi.org/10.1023/A:1010072431120

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1010072431120

Search

Navigation