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All Journal Jurnal Computech & Bisnis (e-Journal) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Venia Restreva Danestiara
Program Studi Informatika, Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences Other

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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

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PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING Venia Restreva Danestiara; Marwondo Marwondo; Nayla Nurul Azkiya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5994

Abstract

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.
Co-Authors Abdillah, Muhammad Syaddad Acep Hendra Akbar, Imannudin Aliman Fauzi Aprilla, Aura Tifa Budiman Dani Setiawan Dirham Triyadi Fadhilah, Agung Nur Graha Prakarsa Himmaniah, Syahira Putri Ikhsan Nur, Muhammad Kaunang, Valencia Marwondo Muhammad Achya Arifudin Nayla Nurul Azkiya Nugraha, Arif Bakti Nur Alamsyah Pangestu, Idham Azis Putra, Raka Deny Adi R. Yadi Rakhman Alamsyah Reni Nursyanti Saputra, Naufal Fajar Setiana, Elia Syahira Putri Himmaniah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga Valencia Claudia Jennifer Kaunang Wiyanudin