2D Autocorrelation Modelling of the Inhibitory Activity of Cytokinin-Derived Cyclin-Dependent Kinase Inhibitors
- 85 Downloads
The inhibitory activity towards p34 cdc 2/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training.
The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.
KeywordsQSAR Autocorrelation vectors Multilinear regression Artificial neural networks Plant hormones
Unable to display preview. Download preview PDF.
- Arris, C.E., Boyle, F.T., Calvert, A.H., Curtin, N.J., Endicott, J.A., Garman, E.F., Gibson, A.E., Golding, B.T., Grant, S., Griffin, R.J., Jewsbury, P., Johnson, L.N., Lawrie, A.M., Newell, D.R., Noble, M.E.M., Sausville, E.A., Schultz, R., Yu, W., 2000. Identification of novel purine and pyrimidine cyclin-dependent kinase inhibitors with distinct molecular interactions and tumor cell growth inhibition profiles. J. Med. Chem. 43, 2797–2804.CrossRefPubMedGoogle Scholar
- Bauknecht, H., Zell, A., Bayer, H., Levi, P., Wagener, M., Sadowski, J., Gasteriger, J., 1996, Locating biologically active compounds in medium-sized heterogeneous datasets by dopological autocorrelation vectors: Dopamine and benzodiazepine agonist. J. Chem. Inform. Comput. Sci. 36, 1205–1213.CrossRefGoogle Scholar
- Demuth, H., Beale, M., 2003a. Neural Network Toolbox User's Guide for Use with MATLAB, 4th edn. The Mathworks Inc., Massachusetts, pp. 51–61, Chapter 5.Google Scholar
- Demuth, H., Beale, M., 2003b. Neural Network Toolbox User's Guide for Use with MATLAB, 4th edn. The Mathworks Inc., Massachusetts, pp. 9–23, Chapter 8.Google Scholar
- Devillers, J., 1999. Autocorrelation descriptors for modelling (eco)toxicological endpoints. In: Devillers, J., Balaban, A.T. (Eds.), Topological Indices and Related Descriptors in QSAR and QSPR. Gordon and Breach Science Publishers, pp. 595–612.Google Scholar
- Frank, J., 1993. Seiler Research Laboratory, MOPAC version 6.0. U.S. Air Force Academy.Google Scholar
- Hemmateenejad, B., Akhond, M., Miri, R., Shamsipur, M., 2003. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: Application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous). J. Chem. Inform. Comput. Sci. 43, 1328–1334.CrossRefGoogle Scholar
- Kolmogorov, A.N., 1957. Doklady Akademiia Nauk SSSR. 114, 953–954.Google Scholar
- Kowalsky, R.B., Wold, S., 1982. Pattern recognition in chemistry. In: Krishnaiah, P.R., Kamal, L.N. (Eds.), Handbook of Statistics. North-Holland, Amsterdam, pp. 673–697.Google Scholar
- Kubinyi, H., 1993. QSAR: Hansch Analysis and Related Approaches. VCH, New York.Google Scholar
- Moreau, G., Broto, P., 1980a. Autocorrelation of a topological structure: A new molecular descriptor. Nouv. J. Chim. 4, 359–360.Google Scholar
- Moreau, G., Broto, P., 1980b. Autocorrelation of Molecular structures: Application to SAR studies. Nouv. J. Chim. 4, 757–764.Google Scholar
- StatSoft Inc, 2001. STATISTICA (data analysis software system), version 6. www.statsoft.com.
- StatSoft Inc, 2004. Electronic Statistics Textbook. StatSoft, Tulsa, OK, web: http://www.statsoft.com/textbook/stathome.html.
- The MathWorks Inc. (2002). MATLAB version 6.5. www.mathworks.com.
- Todeschini, R., Consonni, V., 2000. Handbook of Molecular Descriptors. Wiley-VCH, Weinheim.Google Scholar
- Todeschini, R., Consonni, V., Pavan, M., 2003. DRAGON, version 2.1.Google Scholar