📅 10 March 2024
DOI: 10.62411/jcta.10129
Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.
🔖 Keywords
#ChEMBL; Machine-learning; Molecular descriptor; QSAR; Virtual screening
ℹ️ Informasi Publikasi
Tanggal Publikasi
10 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 4, Tahun 2024
📝 HOW TO CITE
Noviandy, Teuku Rizky; Nisa, Khairun; Idroes, Ghalieb Mutig; Hardi, Irsan; Sasmita, Novi Reandy, "Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM," Journal of Computing Theories and Applications, vol. 1, no. 4, Mar. 2024.
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