QSAR study of some important drugs to predict their lethal dose for children

Document Type : Original Article

Authors

1 Department of Chemistry, Faculty of Sciences, Shahrekord University, P. O. Box 115, Shahrekord, Iran

2 Department of Chemistry, Faculty of Sciences, Shahrekord University, Shahrekord, Iran

10.22036/cr.2021.218394.1112

Abstract

In this study, prediction of average lethal dose of important drugs for children using molecular descriptors and application of Quantitative Structure-Activity Relationship (QSAR) models by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models have been investigated separately. After getting a lot of descriptors the Stepwise regression method was used to reduce the number of descriptors (variables) and the best results were obtained with 8 descriptors. Multivariate linear regression model was then used to predict the lethal dose of the drugs, which yielded almost good results and the parameters R2, Q2 and RMSE for this model were calculated and reported as 0.894, 12.15 and 0.882, respectively. Also by using artificial neural network a better model with correlation coefficients of training, test, validation and total groups were calculated 0.984, 0.994, 0.999 and 0.983, respectively, indicating good validity of this method for Predicting the lethal dose of other similar drugs for children.

Graphical Abstract

QSAR study of some important drugs to predict their lethal dose for children

Keywords

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History

  • Receive Date: 05 February 2020
  • Revise Date: 02 June 2021
  • Accept Date: 21 August 2021

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