Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery
Teuku Rizky Noviandy, Aga Maulana, Ghazi Mauer Idroes, Nur Balqis Maulydia, Mohsina Patwekar, Rivansyah Suhendra, Rinaldi Idroes
Abstract
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.