Modeling and prediction of activity anti-HIV molecules using soft computing techniques
Mohamed Kissi, Mohammed Ramdani · International Journal of Applied Research on Information Technology and Computing · 2010
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.