A Machine Learning-Based Virtual Screening for Natural Compounds Potential on Inhibiting Acetylcholinesterase in the Treatment of Alzheimer’s Disease

¹ Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta, 55281
² Eijkman Center for Molecular Biology, National Research and Innovation Agency, Jakarta, Indonesia

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disease caused by neural cell death, characterized by the overexpression of acetylcholinesterase (AChE) and extracellular deposition of amyloid plaques. Currently, most of the FDA-approved AChE-targeting drugs can only relieve AD symptoms. There is no proven treatment capable to stop AD progression. Many natural products are isolated from several sources and analyzed through preclinical and clinical trials for their neuroprotective effects in preventing and treating AD. Therefore, this study aims to explore and determine potential candidates from natural bioactive compounds and their derivatives for AD treatment targeting AChE. In this study, feature extraction was carried out on 1730 compounds from six plants resulting from literature studies with limitations on international journals with a minimum publication year of 2018 and database searches, then classified using machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). Hit compounds predicted to be active and inactive in the selected model were then processed through ensemble modelling. From 1730 compounds, there are 986 predicted active compounds and 370 predicted inactive compounds in the LR and RF ensemble modelling. Quercetin, Kaempferol, Luteolin, Limonene, γ-Terpinene, Nerolidol, and Linalool predicted active found overlapping in two to three plants in both LR and RF models.