Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery


  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, 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
  • Nur Balqis Maulydia Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Mohsina Patwekar Department of Pharmacology, Luqman College of Pharmacy, Karnataka 585102, India
  • Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Rinaldi Idroes Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia



AChE, Feature selection, Genetic algorithm, LightGBM, Machine learning, QSAR


This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.


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How to Cite

Noviandy, T. R., Maulana, A., Idroes, G. M., Maulydia, N. B., Patwekar, M., Suhendra, R., & Idroes, R. (2023). Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery. Malacca Pharmaceutics, 1(2), 48–54.