THESIS

Enhancing the Prediction of Inhibitor Activity Against Hepatitis C Virus NS5B Through the

Utilization of LightGBM and Bayesian Optimization

ABSTRACT

This study focuses on developing a predictive model for hepatitis C virus (HCV) NS5B inhibitor activity using the Light Gradient Boosting Machine (LightGBM) algorithm. The primary goal is to enhance the accuracy of inhibitor activity predictions, a crucial step in drug discovery for HCV. The study utilizes a molecular dataset comprising 3011 samples, collected from the ChEMBL database. This dataset is divided into 90% training data and 10% test data, resulting in 1503 compounds in the training process and 168 compounds for testing. The process of selecting molecular descriptors involved several stages, including selection based on variance values, multicollinearity, and Recursive Feature Elimination (RFE), resulting in the 50 most relevant molecular descriptors. The constructed LightGBM model employs Bayesian Optimization for hyperparameter tuning. Efforts to improve the model’s predictive performance involved combining several LightGBM models using a voting approach, with evaluation using the coefficient of determination (R’) and root mean squared error (RMSE). The model that has been built is used to predict test data. The results show an increase in the model’s predictive performance when the three LightGBM models are combined, proven by evaluation on test data which obtained the highest R2 value of 0.760 and the lowest RMSE of 0.637. Model validation was conducted through Y-Scrambling techniques, demonstrating that the model’s performance in predicting HCV NSSB inhibitor activity is based on real relationships and not coincidental. SHAP (SHapley Additive exPlanations) analysis was implemented to understand the contribution of each molecular descriptor to the model’s predictions. This analysis helped identify the most influential molecular descriptors, such as MDEC-33 and SpMaxl_Bh(e), providing insights into the molecular characteristics that play a role in inhibiting HCV NS5B. Utilizing SHAP visualizations, ncluding bar charts and bee swarm plots, offered deeper understanding of the influence of each descriptor on the model’s predictions. The study concludes that the combined approach of multiple LightGBM models, coupled with SHAP analysis, represents a significant advancement in predicting the activity of HCV NS5B inhibitors.

PUBLICATION

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