PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING

The dengue virus experiences rapid mutation and genetic variability, posing challenges in developing effective antiviral therapies. This study explores the prediction of binding affinities between potential antiviral drug inhibitors and the NS2B-NS3 protease of the dengue virus using machine learning models. Molecular docking simulations were conducted with AutoDock Vina to generate interaction data between viral proteins and ligands. The generated datasets were used to train several machine learning models, including Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), and Extreme Gradient Boosting Regressor (XGBoost Regressor). The RF Regressor model demonstrated the highest accuracy in predicting binding affinities, measured through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient (R). However, the XGBoost Regressor and SVR models showed better generalization in practical scenarios. This study highlights the potential of machine learning to optimize the drug discovery process and provides significant insights into antiviral drug development for dengue fever.