Interpretable machine learning approach to predict Hepatitis C virus NS5B inhibitor activity using voting-based LightGBM and SHAP

Abstract

Hepatitis C is a pressing global health issue that urgently requires the development of effective antiviral medications. In this study, we focus on targeting the Hepatitis C virus non-structural protein 5B (NS5B) polymerase, a key enzyme in viral RNA replication, to hinder the viral life cycle and reduce viral load. We introduce a computational approach that combines multiple LightGBM models to predict the bioactivity of Hepatitis C virus NS5B inhibitors with enhanced performance. By leveraging a voting mechanism, we achieve a higher predictive performance that surpasses individual LightGBM models. Our model achieves an R-squared (R²) value of 0.760, indicating strong predictive capability, along with a root mean squared error (RMSE) of 0.637, a mean absolute error (MAE) of 0.456, and a Pearson correlation coefficient (PCC) of 0.872, demonstrating the model's precision in predicting inhibitor potency and its strong linear correlation with experimental values. To enhance the interpretability of the model, we performed SHAP analysis, which identified critical molecular features influencing bioactivity and facilitated a deeper understanding of the model's decision-making process. Validation through Y-Scrambling tests confirmed that our model's accuracy significantly exceeds what would be expected by random chance alone, ensuring its robustness and reliability. This study demonstrates the power of ensemble machine learning in computational chemistry and drug design, offering a methodologically transparent and interpretable framework for predicting compound potency, which is critical for virtual screening in early-stage drug development. Our approach not only accelerates the discovery of potent NS5B inhibitors but also provides insights into the molecular determinants of bioactivity, underscoring the potential of machine learning in advancing antiviral research. Future research should focus on integrating QSAR modeling with other computational methods and experimental validation to fully realize the potential of this approach in discovering novel HCV therapeutics.