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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods
Mehdi Rajabi1, Fatemeh Shafiei1
1Department of Chemistry, Science Faculty, Arak Branch, Islamic Azad University, Arak, Iran.
Aim And Objective:
Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure-Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors.
Materials And Methods:
A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities.
Results:
The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263.
Conclusion:
The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.
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