Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Hepatitis C virus (HCV) NS5B polymerase is a critical target for antiviral drug development.
• Non-nucleoside analogue inhibitors (NNIs) targeting the NNI III binding site are a promising class of HCV therapeutics.
• Understanding the structure-activity relationships of these inhibitors is crucial for optimizing their efficacy.

Purpose of the Study:

• To develop quantitative structure-activity relationship (QSAR) models for predicting the inhibitory activity of 333 HCV NS5B polymerase inhibitors.
• To identify key molecular descriptors that govern the bioactivity of these inhibitors.
• To provide insights for the rational design of novel HCV NS5B polymerase inhibitors.

Main Methods:

• Calculation of global and 2D property autocorrelation descriptors using ADRIANA.Code.
• Selection of significant descriptors via Pearson correlation analysis.
• Development of QSAR models using multilinear regression (MLR) and support vector machine (SVM) on training and test sets.
• Validation of models using random splitting and Kohonen's self-organizing map (SOM).

Main Results:

• Developed QSAR models accurately predicted the inhibitory activity of HCV NS5B polymerase inhibitors.
• The best model achieved a correlation coefficient of 0.91 for the test set.
• Molecular complexity, hydrogen bonding donors (HDon), and water solubility (log S) were identified as critical descriptors.
• Electrostatic and charge properties also significantly influenced ligand-protein interactions.

Conclusions:

• QSAR models are effective tools for predicting the inhibitory activity of HCV NS5B polymerase inhibitors.
• Specific molecular descriptors significantly correlate with bioactivity, guiding future drug design.
• Structural analysis of inhibitor-polymerase interactions validates the importance of identified molecular descriptors.