Bulletin of Mathematical Biology

, Volume 68, Issue 4, pp 735–751 | Cite as

2D Autocorrelation Modelling of the Inhibitory Activity of Cytokinin-Derived Cyclin-Dependent Kinase Inhibitors

  • Maykel Pérez González
  • Julio Caballero
  • Aliuska Morales Helguera
  • Miguel Garriga
  • Gerardo González
  • Michael FernándezEmail author
Original Article


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.


QSAR Autocorrelation vectors Multilinear regression Artificial neural networks Plant hormones 


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Copyright information

© Society for Mathematical Biology 2006

Authors and Affiliations

  • Maykel Pérez González
    • 1
    • 2
  • Julio Caballero
    • 3
    • 4
  • Aliuska Morales Helguera
    • 2
    • 5
  • Miguel Garriga
    • 6
  • Gerardo González
    • 6
  • Michael Fernández
    • 3
    • 4
    Email author
  1. 1.Unit of Service, Drug Design DepartmentExperimental Sugar Cane Station “Villa Clara-Cienfuegos,”RanchueloVilla ClaraCuba
  2. 2.Chemical Bioactive CenterCentral University of Las VillasSanta Clara, Villa ClaraCuba
  3. 3.Molecular Modeling Group, Center for Biotechnological Studies, Faculty of AgronomyUniversity of Matanzas, MatanzasMatanzasCuba
  4. 4.Probiotic Group, Center for Biotechnological Studies, Faculty of AgronomyUniversity of Matanzas, MatanzasMatanzasCuba
  5. 5.Department of Chemistry, Faculty of Chemistry and PharmacyCentral University of Las VillasSanta Clara, Villa ClaraCuba
  6. 6.Plant Biotechnology Group, Center for Biotechnological Studies, Faculty of AgronomyUniversity of Matanzas, MatanzasMatanzasCuba

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