QSAR modeling of indole-based HIV attachment inhibitors

QSAR modeling of indole-based HIV attachment inhibitors

Poster

Joanna Stoycheva1, Ismail Hdoufane2, Katarika Josifovska3
1Faculty of Chemistry and Pharmacy, Sofia University “Saint Kliment Ohridski”, Bulgaria
2 Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
3Institute of Chemistry, Faculty of Natural Science and Mathematics, Ss. Cyril and Methodius University, Skopje, North Macedonia

Human immunodeficiency virus-1 (HIV-1) glycoprotein120 (gp120) is a key target for treatment of Аcquired immunodeficiency syndrome (AIDS). A large number of inhibitors are being designed for this target in order to find safe and effective drugs. In the present study, quantitative structure-activity relationship (QSAR) models established on 128 gp120 indole-based attachment inhibitors have been developed. Chemometrics techniques including multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods were used to set up QSAR models in order to explain the structural requirements of HIV-1 gp120 inhibitory activity. The prediction performance of each developed model was evaluated and the results reveal that the predictive power of the SVM model is slightly superior to those of the MLR and ANN models. The established models could improve, diversify, and accelerate the HIV drug development process.
Key words: HIV-1 attachment inhibitors, gp120, QSAR