Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma

Authors

  • Teuku Rizky Noviandy Department of Information Systems, Faculty of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Eva Imelda Department of Ophthalmology, General Hospital Dr. Zainoel Abidin, Banda Aceh 23126, Indonesia; Department of Ophthalmology, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Ghazi Mauer Idroes Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Rinaldi Idroes School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/mp.v3i1.271

Keywords:

CA-II inhibitors, Virtual screening, Glaucoma drug discovery, Artificial intelligence

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.

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Published

2025-03-04

How to Cite

Noviandy, T. R., Imelda, E., Idroes, G. M., Suhendra, R., & Idroes, R. (2025). Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma. Malacca Pharmaceutics, 3(1), 32–41. https://doi.org/10.60084/mp.v3i1.271

Issue

Vol. 3 No. 1 (2025): March 2025

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Article

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Copyright (c) 2025 Teuku Rizky Noviandy, Eva Imelda, Ghazi Mauer Idroes, Rivansyah Suhendra, Rinaldi Idroes

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