Background:Human immunodeficiency virus (HIV) is an infective agent that causes
an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for
preventing the progression of the disease is required.Objective:This study aims to construct quantitative structure-activity relationship (QSAR) models,
molecular docking and newly rational design of colchicine and derivatives with anti-HIV
activity.Methods:A data set of 24 colchicine and derivatives with anti-HIV activity were employed to
develop the QSAR models using machine learning methods (e.g. multiple linear regression
(MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular
docking.Results:The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm
and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities
as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one
out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed
MLR and SVM methods that displayed LOO−CV
2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV
set, and
Ext
2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular
docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting
residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine
derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design
of colchicine derivatives using informative QSAR and molecular docking was proposed.Conclusion:These findings serve as a guideline for the rational drug design as well as potential
development of novel anti-HIV agents.