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Insight prediction of receptor binding activity of a set of benzamide derivatives using hybrid QSAR models: GA‐MLR and GA‐SVR
Author(s) -
Van Tat Pham,
Nhung Nguyen Thi Ai
Publication year - 2020
Publication title -
vietnam journal of chemistry
Language(s) - English
Resource type - Journals
eISSN - 2572-8288
pISSN - 0866-7144
DOI - 10.1002/vjch.201900152
Subject(s) - quantitative structure–activity relationship , benzamide , support vector machine , multivariate statistics , molecular descriptor , bayesian multivariate linear regression , linear regression , regression analysis , artificial intelligence , machine learning , computer science , mathematics , chemistry , stereochemistry
In this study, we developed the hybrid QSAR models (HQSAR) for a set of benzamide derivatives by combining genetic algorithms with multivariate regression and support vector machine learning techniques. The genetic algorithm has assisted the selecting process of 2D and 3D molecular descriptors to get a globally optimal HQSAR GA‐MLR model with k = 7. The hybrid support vector regression model (HQSAR GA‐SVR ) received from the selected descriptors of the multivariable regression model (HQSAR GA‐MLR ) has been operated to predict the pIC 50 activity of validation and prediction groups with MARE% of 0.8492 % and 2.8411 %. The hybrid support vector technique has improved the efficiency of the predictability of the multivariate regression model. The predicted activities pIC 50 of benzamide derivatives resulting from the HQSAR GA‐SVR model are reliable enough and in good agreement with experimental data.