3D QSAR kNN-MFA studies on thiouracil derivatives as hepatitis C virus inhibitors

Abstract

The development of new therapies to treat hepatitis C virus (HCV) infection effectively is currently an intensive area of research. To achieve this objective quantitative structure–activity relationship (QSAR) study was carried as it provides the rationale for the changes in the structure to have more potent analogs. In this article, we report 3D QSAR studies for the set of 50 HCV NS5B RNA-dependent RNA polymerase inhibitors using k-Nearest Neighbor Molecular Field Analysis (kNN-MFA) method combined with various selection procedures. By using kNN-MFA approach, various 3D QSAR models were generated to study the effect of steric and electrostatic descriptors on anti-HCV activity. The model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.85 and 0.75, respectively. The steric descriptors at the grid points S_430, S_1065, and S_1165 play an important role in the design of new molecule. It also suggests the importance of aromatic or large bulky ring substituent at R1 to increase the HCV inhibitory activity as well as large bulky substituent at R2 reduces activity. This model was found to yield reliable clues for further optimization of thiouracil derivatives in the data set.