Title:Machine Learning, Molecular Modeling, and QSAR Studies on Natural Products Against Alzheimer’s Disease
Volume: 28
Issue: 38
Author(s): Érika Paiva de Moura, Natan Dias Fernandes, Alex France Messias Monteiro, Herbert Igor Rodrigues de Medeiros, Marcus Tullius Scotti and Luciana Scotti*
Affiliation:
- Federal University of Paraiba, Health Sci. Center, 58051-900, Joao Pessoa, PB,Brazil
Keywords:
Alzheimer's disease, in silico, molecular docking, molecular dynamics, QSAR, molecular learning.
Abstract:
Background: Alzheimer's disease (AD) is a very common neurodegenerative
disorder in individuals over 65 years of age; however, younger individuals can also be affected
due to brain damage.
Introduction: The general symptoms of this disease include progressive loss of memory,
changes in behavior, deterioration of thinking, and gradual loss of ability to perform daily
activities. According to the World Health Organization, dementia has affected more
than 50 million people worldwide, and it is estimated that there are 10 million new cases
per year, of which 70% are due to AD.
Methods: This paper reported a review of scientific articles available on the internet
which discuss in silico analyzes such as molecular docking, molecular dynamics, and
quantitative structure-activity relationship (QSAR) of different classes of natural products
and their derivatives published from 2016 onwards. In addition, this work reports
the potential of fermented papaya preparation against oxidative stress in AD.
Results: This research reviews the most recent studies on AD, computational analysis
methods used in proposing new bioactive compounds and their possible molecular targets,
and finally, the molecules or classes of natural products involved in each study.
Conclusion: Thus, studies like this can orientate new research works on neurodegenerative
diseases, especially AD.
