Design of drugs with potential antitumoral activity


  • Lisandra Rodríguez-Batista Instituto Nacional de Reservas Estatales, Holguín
  • Julio César Escalona-Arranz Universidad de Oriente
  • Julio Alberto Rojas-Vargas Universidad de Oriente
  • Edgar Hernández-Sosa Universidad de Oriente


Cancer is a health problem, resulting in the first cause of death worldwide. In the present paper, a Quantitative Structure-Activity Relationship (QSAR) study was developed as a tool for the design of new drugs with potential antitumor activity. Discriminant Linear Analysis and Neural Network were the mathematics methods used to estimate the activity of in a data set consisting in 300 compounds. The biological activity, extracted from the US National Cancer Institute was divided by cluster analysis in a training and prediction series. A model with 10 variables and 84,33 % of correct classification was obtained by a discriminant function meanwhile, the neural network tested with the same number of variables resulted in a 89,67 % of accuracy. Also was calculated the contribution of different structural fragments on the cytostatic activity, and quantified their contribution. Six new compounds were designed predicting a good antitumor activity. In general, the predictive quality of the neural network model was higher than the linear discriminant.

Keywords: cancer, QSAR study, antitumor activity.



How to Cite

Rodríguez-Batista, L., Escalona-Arranz, J. C., Rojas-Vargas, J. A., & Hernández-Sosa, E. (2016). Design of drugs with potential antitumoral activity. Revista Cubana De Química, 28(2), 579–594. Retrieved from




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