An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates

Abstract

Hepatitis C virus (HCV) infection affects over 71 million people worldwide, leading to severe liver diseases, including cirrhosis and hepatocellular carcinoma. The virus’s high mutation rate complicates current antiviral therapies by promoting drug resistance, emphasizing the need for novel therapeutics. Traditional high-throughput screening (HTS) methods are costly, time-consuming, and prone to false positives, underscoring the necessity for more efficient alternatives. Machine learning (ML), particularly quantitative structure–activity relationship (QSAR) modeling, offers a promising solution by predicting compounds’ biological activity based on chemical structures. However, the “black-box” nature of many ML models raises concerns about interpretability, which is critical for understanding drug action mechanisms. To address this, we propose an explainable multi-model stacked classifier (MMSC) for predicting hepatitis C drug candidates. Our approach combines random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), and k-nearest neighbors (KNN) using a logistic regression meta-learner. Trained and tested on a dataset of 495 compounds targeting HCV NS3 protease, the model achieved 94.95% accuracy, 97.40% precision, and a 96.77% F1-score. Using SHAP values, we provided interpretability by identifying key molecular descriptors influencing the model’s predictions. This explainable MMSC approach improves hepatitis C drug discovery, bridging the gap between predictive performance and interpretability while offering actionable insights for researchers.