Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

Authors

  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Khairun Nisa Clinical Pharmacy, Stikes Jabal Ghafur, Sigli 24151, Indonesia
  • Ghalieb Mutig Idroes Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, Indonesia
  • Irsan Hardi Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, Indonesia
  • Novi Reandy Sasmita Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.62411/jcta.10129

Keywords:

ChEMBL, Machine-learning, Molecular descriptor, QSAR, Virtual screening

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.

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Published

2024-03-10

How to Cite

Noviandy, T. R., Nisa, K., Idroes, G. M., Hardi, I., & Sasmita, N. R. (2024). Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM. Journal of Computing Theories and Applications, 1(4), 358–367. https://doi.org/10.62411/jcta.10129