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Cited by 56Computational Drug Discovery MethodsCholinesterase and Neurodegenerative DiseasesMachine Learning in Materials Science

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

Dhamodharan, G., Mohan, C. Gopi·2022·Molecular Diversity
DOI 10.1007/s11030-021-10282-8
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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 (R 2 = 0.8688, q 2 = 0.8600) and BACE1 (R 2 = 0.8177, q 2 = 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 (r 2 = 0.8469, r 2pred = 0.8138) and BACE1 (r 2 = 0.7805, r 2pred = 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.

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  • ML has been used to identify novel inhibitors of beta-secretase (BACE1), a protein involved in Alzheimer's disease.

    “Another example is the identification of novel inhibitors of beta-secretase (BACE1), a protein involved in the development of Alzheimer’s disease [23] by using an ML algorithm.”

    From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [23]

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Authors
Dhamodharan, G., & Mohan, C. G.
Affiliations
Amrita Vishwa Vidyapeetham (IN)
Corresponding author
Mohan, C. Gopi
ORCID
Mohan, C. Gopi
Venue
Molecular Diversity
Publisher
Springer Science and Business Media LLC
Published
2021-07-29
Volume
26
Issue
3
Pages
1501-1517
ISSN
  • 1381-1991 (ISSN-L)
  • 1573-501X
DOI
10.1007/s11030-021-10282-8
OpenAlex
W3186363990
Cited by
56
References
41
Citations by year
2026: 16 · 2025: 10 · 2024: 16 · 2023: 11 · 2022: 3
FWCI
3.95
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Molecular Diversity (publishedVersion)
  • PubMed (publishedVersion)
Funders
Department of Biotechnology, Government of West Bengal (BT/PR21018/BID/7/776/2016)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
acetylcholinesterase inhibitors, BACE1 inhibitors, dual enzyme inhibitors, Alzheimer's disease, multi-target drug design, 2D-QSAR, molecular descriptors, support vector machine, artificial neural networks, genetic function approximation, lead compound design
MeSH terms
Machine Learning, Acetylcholinesterase, Alzheimer Disease, Cholinesterase Inhibitors, Humans, Aspartic Acid Endopeptidases, Quantitative Structure-Activity Relationship, Amyloid Precursor Protein Secretases, Molecular Docking Simulation

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