Artificial Neural Networks Analysis Used to Evaluate the Molecular Interactions between Selected Drugs and Human Cyclooxygenase2 Receptor.
OBJECTIVE
A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Artificial neural networks (ANNs) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. We proposed a structure that obtains binding energy using physicochemical molecular descriptions of the selected drugs.


METHODS
The set of 33 drugs with their binding energy to cyclooxygenase enzyme (COX2) in hand, from different structure groups, were considered. 27 physicochemical property descriptors were calculated by standard molecular modeling. Binding energy was calculated for each compound through docking and also ANN. A multi-layer perceptron neural network was used.


RESULTS
The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between binding energy observed and calculated. The final model possessed a 27-4-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.973, 0.956 and 0.950, respectively.


CONCLUSIONS
RESULTS show that docking results and ANN data have a high correlation. It was shown that ANN is a strong tool for prediction of the binding energy and thus inhibition constants for different drugs in very short periods of time.
Department of Electrical Engineering, Ferdosi University of Mashad, Mashad, Iran.
Ali
A
Tayarani
School of Pharmacy, Mashhad University of Medical Sciences, Mashad, Iran.
Ali
A
Baratian
Department of Electrical Engineering, Ferdosi University of Mashad, Mashad, Iran.
Mohammad-Bagher
MB
Naghibi Sistani
School of Pharmacy, Mashhad University of Medical Sciences, Mashad, Iran.
Mohammad Reza
MR
Saberi
School of Pharmacy, Mashhad University of Medical Sciences, Mashad, Iran.
Zeinab
Z
Tehranizadeh
Artificial Neural Networks; Binding Energy; COX2; Cyclooxygenase 2; Docking