Background: Alzheimer’s disease (AD) is one of the most common lethal
neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease
lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates
the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported
underlying the association of the two disorders, diabetes and dementia, one among which is the
insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides.
Methods: The present study is aimed to generate accurate classification models using machine
learning techniques, which could identify IDE modulators from a bioassay dataset consisting of IDE
inhibitors as well as non-inhibitors. The identified compounds were subjected to docking and
Molecular dynamics (MD) studies for an in-depth analysis of the binding modes along with the
complex stability. This study proposes that the identified potential active compounds, STK026154
(PubChem ID: CID2927418) with Glide score of -7.70 kcal/mol and BAS05901102 (PubChem ID:
CID3152845) with Glide score of -7.06 kcal/mol, could serve as promising leads for the
development of novel drugs against AD.
Conclusion: The present study shows that such in silico
approaches can be effectively used to
discover and select active compounds from unseen data for accelerated drug development process.
The machine learning models generated in the present study were used to screen Traditional Chinese
Medicine (TCM) database to identify the phytocompounds already been reported to have therapeutic
effects against AD.