[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119287-en":3,"doc-seo-119287-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119287,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Prediction of Inhibitor Binding Affinity and Molecular Interactions in MPro Dengue Using Machine Learning","The dengue virus undergoes rapid mutation and genetic variability, making antiviral therapy development difficult and reducing the reliability of existing approaches. This study predicts binding affinities between candidate antiviral inhibitors and the dengue NS2B–NS3 protease using machine learning. Molecular docking data were generated with AutoDock Vina to create protein–ligand interaction datasets, then used to train Random Forest Regressor, SVR, and XGBoost Regressor models. The RF Regressor achieved the best overall accuracy using MAE, RMSE, and Pearson correlation. XGBoost and SVR showed stronger generalization in practical settings.","PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING  \nVenia Restreva Danestiara1*; Marwondo2; Nayla Nurul Azkiya3  \nInformatics Study Program1, 2, 3  \nUniversitas Informatika dan Bisnis Indonesia, Bandung, Indonesia1, 2, 3  \n[www.unibi.ac.id](www.unibi.ac.id1)[1](www.unibi.ac.id1), 2, 3  \n[veniarestreva@unibi.ac.id](veniarestreva@unibi.ac.id1)[1](veniarestreva@unibi.ac.id1)* , [marwondo@unibi.ac.id](marwondo@unibi.ac.id2)[2](marwondo@unibi.ac.id2), [naylanurulazkiya23@student.unibi.ac.id](naylanurulazkiya23@student.unibi.ac.id3)[3](naylanurulazkiya23@student.unibi.ac.id3)  \n(*) Corresponding Author  \n(Responsible for the Quality of Paper Content)  \nThe creation is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License.  \nAbstract—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.  \nKeywords: binding affinities, dengue virus, machine learning, molecular docking, NS2B-NS3 protease.  \nIntisari—Virus dengue mengalami mutasi cepat dan variabilitas genetik, yang menimbulkan tantangandalampengembangan terapi antivirus yang efektif. Penelitian ini mengeksplorasiprediksi afinitaspengikatanantara inhibitor obat antiviral potensial dan protease NS2B-NS3 virus dengue menggunakan model pembelajaran mesin. Simulasi docking molekuler dilakukan dengan AutoDock Vina untuk menghasilkan data interaksi antara protein virus dan ligan. Dataset yang dihasilkan digunakan untuk melatih beberapa model pembelajaran mesin, termasuk Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), dan Extreme Gradient Boosting Regressor (XGBoost Regressor). Model RF Regressor menunjukkan akurasitertinggi dalam memprediksi afinitas pengikatan, diukur dengan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Pearson Correlation Coefficient (R). Namun, model XGBoost Regressor dan SVR menunjukkan generalisasi yang lebih baik dalam skenario praktis. Penelitian ini menyoroti potensipembelajaran mesin untuk mengoptimalkanproses penemuanobat dan memberikan wawasan penting dalampengembangan obat antivirus untuk demam dengue.  \nKata Kunci: afinitas pengikatan, virus dengue, pembelajaran mesin, docking molekuler, protease NS2B-NS3.  \nINTRODUCTION  \nDengue fever remains a significant public health challenge in Indonesia. In 2023, there were 114,435 cases and 894 deaths, while in the first eight weeks of 2024, there were 15,977 cases and 124 deaths. Notably, the DENV-3 and DENV-2 serotypes dominate, contributing 53.4% and 38.6%  \nof total cases, respectively, with a higher risk of severe complications such as dengue shock syndrome (DSS) [1] . The genetic variability of the pathogen and the limitations of antiviral treatments exacerbate the situation, emphasizing the urgent need for effective therapies [2] .  \nThe NS2B-NS3 protease plays a crucial role in dengue virus replication, functioni","cbCaikRiy3LMgXzZ","https://ap.wps.com/l/cbCaikRiy3LMgXzZ","pdf",1498421,1,"English","en",105,"# Introduction\n## Dengue as a public health challenge\n## Role of the NS2B–NS3 protease and therapeutic target rationale\n## Research gap and need for improved datasets\n## Objectives of the study\n# Molecular docking for antiviral evaluation","[{\"question\":\"Why is the dengue virus difficult to treat with antiviral drugs?\",\"answer\":\"Dengue experiences rapid mutation and genetic variability, which undermines the effectiveness of existing antiviral strategies and increases the need for more reliable inhibitors.\"},{\"question\":\"How were binding affinity prediction datasets generated in this study?\",\"answer\":\"Molecular docking simulations were performed with AutoDock Vina to produce interaction data between viral proteins and candidate ligands, forming training datasets for machine learning models.\"},{\"question\":\"Which machine learning model performed best for binding affinity prediction, and how was it evaluated?\",\"answer\":\"Random Forest Regressor showed the highest accuracy, assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson correlation coefficient (R).\"}]","Prediction of Inhibitor Binding Affinity and Molecular Interactions in MPro Dengue Using Machine Learning | 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is the dengue virus difficult to treat with antiviral drugs?","Question",{"text":74,"@type":75},"Dengue experiences rapid mutation and genetic variability, which undermines the effectiveness of existing antiviral strategies and increases the need for more reliable inhibitors.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were binding affinity prediction datasets generated in this study?",{"text":79,"@type":75},"Molecular docking simulations were performed with AutoDock Vina to produce interaction data between viral proteins and candidate ligands, forming training datasets for machine learning models.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning model performed best for binding affinity prediction, and how was it evaluated?",{"text":83,"@type":75},"Random Forest Regressor showed the highest accuracy, assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson correlation coefficient 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