QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression

Authors

  • Teuku Rizky Noviandy Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, Indonesia
  • Ghifari Maulana Idroes Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
  • Trina Ekawati Tallei Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, Indonesia
  • Dian Handayani Sumatran Biota Laboratory, Faculty of Pharmacy, Universitas Andalas, 25163 Padang, Indonesia
  • Rinaldi Idroes Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/mp.v2i2.226

Keywords:

BACE1, Machine learning, Molecular descriptors, Supervised learning

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.

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Published

2024-09-30

How to Cite

Noviandy, T. R., Idroes, G. M., Tallei, T. E., Handayani, D., & Idroes, R. (2024). QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer’s Disease with Support Vector Regression. Malacca Pharmaceutics, 2(2), 79–85. https://doi.org/10.60084/mp.v2i2.226

Issue

Vol. 2 No. 2 (2024): September 2024

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Article

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Copyright (c) 2024 Teuku Rizky Noviandy, Ghifari Maulana Idroes, Trina Ekawati Tallei, Dian Handayani, Rinaldi Idroes

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