Artificial neural network applied to the discrimination of antibacterial activity by topological methods

Journal of Molecular Structure: THEOCHEM

Volumn 504, Issue 1-3, 2000, Pages 249-259

  Tomas Vert F ^a   Perez Gimenez F ^a   Salabert Salvador, Ma T ^a   Garcia March F J ^a   Jaen Oltra J ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Antibacterial agents; Neural networks; Quantitative structure activity relationship

Indexed keywords

ANTIBIOTIC AGENT; ANTIINFECTIVE AGENT;

ANTIBACTERIAL ACTIVITY; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHEMICAL STRUCTURE; DRUG DISCRIMINATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS;

EID: 0343962606     PISSN: 01661280     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0166-1280(00)00366-3     Document Type: Article

References (22)

1. Kier L.B., Hall L.H. Molecular Connectivity in Structure-Activity Analysis. 1986;Research Studies Press, Letchworth, England.
2. Basak S.C. Use of molecular complexity indices in predictive pharmacology and toxicology: a QSAR approach. Med. Sci. Res. 15:1987;605-609.
3. Basak S.C., Bertelsen S., Grunwald G. Application of graph theoretical parameters in quantifying molecular similarity and structure-activity studies. J. Chem. Inf. Comput. Sci. 34:1994;270-276.
4. Antón Fos G.M., García March F.J., Pérez Giménez F., Salabert Salvador M.T., Cercós del Pozo R.A. Calculation of chromatographic parameters by molecular topology: sulphamides. J. Chromatogr. A. 672:1994;203-211.
5. García March F., Antón Fos G.M., Cercós del Pozo R.A., Pérez Giménez F., Salabert Salvador M.T., Jaen Oltra J. Correlation of pharmacological properties of a group of beta blocker agents by molecular topology. J. Pharm. Pharmacol. 47:1995;232-236.
6. García March F.J., Antón Fos G.M., Pérez Giménez F., Salabert Salvador M.T., Cercós del Pozo R.A., Julian-Ortiz J.V. Prediction of the chromatographic properties of a group of natural phenolic derivates by molecular topology. J. Chromatogr. A. 719:1996;45-51.
7. Pérez Giménez F., Antón Fos G.M., García March F., Salabert Salvador M.T., Cercós del Pozo R.A., Jaen Oltra J. Prediction of chromatographic parameters for some anilines by molecular connectivity. Chromatographia. 41:(3/4):1995;167-174.
8. Salabert Salvador M.T., García March F.J., Pérez Giménez F., Antón Fos G.M., Cercós del Pozo R.A., Jaen Oltra J. Calculation of chromatographic properties of barbiturates by molecular topology. Chromatographia. 41:(11/12):1995;702-706.
9. Cercós del Pozo R.A., Pérez Giménez F., Gálvez Alvarez J., Salabert Salvador M.T., García March F.J., Antón Fos G.M. Correlation of pharmacological properties of a group of hypolipaemic drugs by molecular topology. J. Pharm. Pharmacol. 48:1996;241-245.
10. Stankevich M.I., Stankevich I.V., Zefirov N.S. Topological indexes in organic. Chemi. Usp. Khimii. 57:1988;337-366.
11. Rouvray D.H. Should we have designs on topological indexes? Stud. Phys. Theor. Chem. (Chem. Appl. Topol. Graph Theory). 28:1983;159-177.
12. Balaban A. Chemical graphs. XXXIV. Five new topological indices for the branching of tree-like graphs. Theor. Chim. Acta. 53:1979;355-375.
13. Seybold P.G., May M., Bagal U.A. Molecular structure-property relationships. J. Chem. Educ. 64:1987;575-581.
14. Balaban A.T., Basak S.C., Colburn T., Grunwald G.D. Correlation between structure and normal boiling points of haloalkanes C1-C4 using neural networks. J. Chem. Inf. Comput. Sci. 34:1994;1118-1121.
15. Baskin I.I., Palyulin V.A., Zefirov N.S. Computational neural networks as an alternative to the linear regression analysis in the studies of quantitative structure-property relationships for the case of physico-chemical properties of hydrocarbons. Dokl. Akad. Nauk. 332:1993;713-716.
16. Ghoshal N., Mukhopadhayay S.N., Ghoshal T.K., Achari B. Quantitative structure-activity relationship studies using artificial neural networks. Indian J. Chem. Sect. B. 32:1993;1045-1050.
17. Glen R.C., Rose V.S., Lindon J.C., Ruane R.J., Wilson I.D., Nicholson J.K. Quantitative structure-chromatography relationships: prediction of TLC behavior using theoretically derived molecular properties. J. Planar Chromatogr.-Mod. TLC. 1991;432-438.
18. Kosko B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. 1992;Prentice Hall, Englewood Cliffs, NJ.
19. Maggiora G.M., Elrod D.W., Trenary R.G. Computational neural networks as model-free mapping devices. J. Chem. Inf. Comput. Sci. 32:1992;732-741.
20. Elrod D., Maggiora G.M., Trenary R.G. Applications of neural networks in chemistry. 1. Prediction of electrophilic aromatic substitution reactions. J. Chem. Inf. Comput. Sci. 30:1990;477-484.
21. Zupan J., Gasteiger J. Neural networks: a new method for solving chemical problems or just a passing phase? Anal. Chim. Acta. 248:1991;1-30.
22. Gálvez J., García-Domenech R., de Gregorio Alapont C., de Julián-Ortiz J.V., Popa L. Pharmacological distribution diagrams: A tool for the novo drug design. J. Mol. Graphics. 14:1996;272-276.