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Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.

Authors
  • Dhamodharan, G1
  • Mohan, C Gopi2
  • 1 Computational Biology and Bioinformatics Lab, Center for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala, 682041, India. , (India)
  • 2 Computational Biology and Bioinformatics Lab, Center for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala, 682041, India. [email protected] , (India)
Type
Published Article
Journal
Molecular Diversity
Publisher
Springer-Verlag
Publication Date
Jun 01, 2022
Volume
26
Issue
3
Pages
1501–1517
Identifiers
DOI: 10.1007/s11030-021-10282-8
PMID: 34327619
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R2 = 0.8688, q2 = 0.8600) and BACE1 (R2 = 0.8177, q2 = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r2 = 0.8469, r2pred = 0.8138) and BACE1 (r2 = 0.7805, r2pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules. © 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

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