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
Method: 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
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
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.