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All Journal Narra J GALENICAL : Jurnal Kedokteran dan Kesehatan Mahasiswa Malikussaleh Heca Journal of Applied Sciences Malacca Pharmaceutics Indonesian Journal of Case Reports Grimsa Journal of Science Engineering and Technology
Imelda, Eva
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Physics Public Health Veterinary

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Journal : Malacca Pharmaceutics

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Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma Noviandy, Teuku Rizky; Imelda, Eva; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.271

Abstract

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play a crucial role in this treatment by reducing aqueous humor production. However, existing CA-II inhibitors often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient and targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict CA-II inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple machine learning models, including Support Vector Machine, Gradient Boosting, and Random Forest, we identify SVM as the most effective classifier, achieving the highest accuracy (83.70%) and F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling and hyperparameter optimization. Our findings underscore the potential of machine learning-based virtual screening in accelerating CA-II inhibitor identification and advocate for integrating AI-driven approaches with traditional drug discovery techniques. Future directions include deep learning enhancements and hybrid machine learning-docking frameworks to improve prediction accuracy and facilitate the development of more potent and selective glaucoma treatments.
Co-Authors Adev, Alya Mutiara Adev, Sarra Mutiara Anggreiny, Cut Dini Annisa, Thyfa Emka, Erifa Ananda Fadliyani, Eka Faizah, Siti Fitria, Ulfa Ghassani, Vania Ghazi Mauer Idroes Gianty, Jauza Aqilla Gunawan, Fany Jannah, Siti Rain Lathifah, Lakaeisya SY Lubis, Dara Amalia Mutia, Ulmiza Putri Nuzhatuddin, Fatimah Raudhatul Jannah Rinaldi Idroes Samira, Cut Putri Sari, Cut Refida Sari, Masitha Dewi Sari, Munita Sarra Mutiara Adev Siti Rain Jannah Suhendra, Rivansyah Syahrul Syahrul Teuku Rizky Noviandy Toshniwal, Navneet Shamsundar Toshniwal, Nikhil Toshnowal, Nikhil Zakiaturrahmi, Zakiaturrahmi