Review: A hybrid decision trees-adaptive neuro-fuzzy inference system in prediction of anti-HIV molecules

Authors:
Mohamed Kissi;Mohammed Ramdani
Affiliations:
Equipe MMID, Département de Mathématiques et Informatique, Faculté des Sciences, B.P. 20, 24000 El Jadida, Morocco;Département d'informatique (LIM@II), Faculté des Sciences et Techniques, B.P. 146, 20650 Mohammedia, Morocco
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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 for 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 are superior over the others.