Modeling and prediction of activity anti-HIV molecules using soft computing techniques Kissi Mohameda,*, Ramdani Mohammedb,** aEquipe Modélisation Mathématique et Informatique Décisionnelle (EMMID), Département de Mathématiques et Informatique, Faculté des Sciences, B.P. 20, 24000, El Jadida, Morocco bDépartement d'informatique (LIM@II), Faculté des Sciences et Techniques, B. P. 146, 20650, Mohammedia, Morocco * kissim@gmail.com
** moha@poleia.lip6.fr
Abstract Several works quantitative structure-activity relationships (QSAR) of anti-Human Immunodeficiency Virus (HIV) molecules were studied by different statistical methods and non-linear models. But few studies have used the heuristic methods. In this paper, a hybrid decision trees (DT) and adaptive neuro-fuzzy inference system (ANFIS) is used of the prediction of inhibitory activity of anti-VIH molecules. DT algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict the anti-HIV activity. The model's predictions were compared with other methods and the results indicated that the proposed models in this work is superior over the others. Top Keywords Fuzzy inference system, Decision trees, QSAR, Anti-HIV. Top |