Title:Application of Artificial Neural Networks for the Prediction of Antitumor Activity of a Series of Acridinone Derivatives
VOLUME: 8 ISSUE: 3
Author(s):Marcin Koba
Affiliation:Department of Medicinal Chemistry, Faculty of Pharmacy, Collegium Medicum of Nicolaus Copernicus University, Bydgoszcz, Poland.
Keywords:Acridinones, antitumor activity, artificial neural networks (ANNs), molecular descriptors, sensitivity analysis, acridinone derivative, lipophilicity, imidazoacridinones, triazoloacridinones
Abstract:Artificial neural networks (ANNs) have been applied for the quantitative structure-activity relationships
(QSAR) studies of antitumor activity of acridinone derivatives. Molecular modeling studies were performed with the use
of HyperChem and Dragon computer programs and molecular geometry optimization using MM+ molecular mechanics
and semi-empirical AM1 method, and several molecular descriptors of agents were obtained. A high correlation resulted
between the ANN predicted antitumor activity and that one from biological experiments for the data used in the testing set
of acridinones was obtained with correlation coefficient on the level of 0.9484. Moreover, the sensitivity analysis indicated
that molecular parameters describing geometrical properties as well as lipophilicity of acridinone derivative molecule
are important for acridinones antitumor activity.
