title: Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022. India.
authors.forename: Salma
authors.initials: S
authors.lastname: Jamal
authors.affiliation: Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022. India.
authors.forename: Sukriti
authors.initials: S
authors.lastname: Goyal
authors.affiliation: Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022. India.
authors.forename: Asheesh
authors.initials: A
authors.lastname: Shanker
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067. India.
authors.forename: Abhinav
authors.initials: A
authors.lastname: Grover
mesh_terms: D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D007339:Insulysin / Q000037:antagonists & inhibitors* / Q000378:metabolism*; D000069550:Machine Learning; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation
chemical_list: D007339:Insulysin
keywords: Alzheimer's; diabetes mellitus; insulin-degrading enzyme; machine learning; molecular dynamics; traditional
  Chinese Medicine