Novel, potent and orally bioavailable indolizidinone-derived inhibitors of the hepatitis C virus NS3 protease

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC₅₀) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r²) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC₅₀ values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC₅₀ values were predicted based on the average prediction of ten models.

QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM)

In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC₅₀ values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r²) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline.