Abstract
This work presents an approach that uses both genetic algorithm (GA) and many machine learning algorithms (MLA) for feature selection molecular descriptors in a quantitative structure-activity relationships (QSAR) classification and prediction problem. The MLA is used to evaluate an individual population in the GA process. So the fitness function is introduced and defined by the best accuracy classification of the GA and MLA combination. The proposed approach has been implemented and tested using a data set with experimental value antihuman immunodeficiency virus (anti-HIV) molecules. The classification parameters sensibility is equal to 0.99, specificity is equal to 0.91, and accuracy is equal to 0.98. These results reveal the capacity for achieving data subset of molecular descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.
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Labjar, H., Labjar, N., Kissi, M. (2022). QSAR Anti-HIV Feature Selection and Prediction for Drug Discovery Using Genetic Algorithm and Machine Learning Algorithms. In: Ouaissa, M., Boulouard, Z., Ouaissa, M., Guermah, B. (eds) Computational Intelligence in Recent Communication Networks . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77185-0_12
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