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Identification of potential AChE inhibitors through combined machine-learning and structure-based design approaches

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Title Identification of potential AChE inhibitors through combined machine-learning and structure-based design approaches
 
Creator Ganeshpurkar, Ankit
Singh, Ravi
Singh, Ravi Bhushan
Kumar, Devendra
Kumar, Ashok
Singh, Sushil Kumar
 
Subject Alzheimer’s disease
Amber
Artificial intelligence
Autodock
Cholinesterase
 
Description 619-631
Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disease characterised by dementia.The
depletion of acetylcholine (ACh) is involved the synaptic cleft is responsible for dementia due to neuronal loss. The
acetylcholinesterase (AChE) enzyme isinvolved in the hydrolytic degradation of ACh and its inhibition is therapeutically
beneficial for the treatment in memory loss.The use of machine learning (ML) for the identification of enzyme inhibitors has
recently become popular. It identifies important patterns in the reported inhibitors to predict the new molecules. Hence, in
this study, a set of support vector classifier-based ML models were developed,validated and employed to predict AChE
inhibitors. Further, 247 predicted compounds obtained through PAINS and molecular property filters were docked on the
AChE enzyme. The docking study identified compounds AAM132011183, ART21232619 and LMG16204648 as AChE
inhibitors with suitable ADME properties. The selected compounds produced stable interactions with enzymes in molecular
dynamics studies. The novel inhibitors obtained from the study may be proposed as active leads for AChE inhibition.
 
Date 2022-07-01T11:15:51Z
2022-07-01T11:15:51Z
2022-06
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscpr.res.in/handle/123456789/60013
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJBB Vol.59(6) [June 2022]