QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

Authors

  • 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
  • Talha Bin Emran Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
  • Ghazi Mauer Idroes Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Rinaldi Idroes Department of Chemistry, Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/hjas.v1i1.12

Keywords:

Beta-secretase 1, Ensemble machine learning, Molecular descriptors, QSAR

Abstract

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.

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References

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Published

2023-05-30

How to Cite

Noviandy, T. R., Maulana, A., Emran, T. B., Idroes, G. M. and Idroes, R. (2023) “QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine Learning Algorithms”, Heca Journal of Applied Sciences, 1(1), pp. 1–7. doi: 10.60084/hjas.v1i1.12.

Issue

Vol. 1 No. 1 (2023): June 2023

Section

Articles

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Copyright (c) 2023 Teuku Rizky Noviandy, Aga Maulana, Talha Bin Emran, Ghazi Mauer Idroes, Rinaldi Idroes

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.