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

Publication Type : Journal Article

Thematic Areas : Nanosciences and Molecular Medicine

Publisher : Molecular Diversity

Source : Molecular Diversity (Article in Press) (2021)

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Keywords : acetylcholinesterase, Alzheimer’s disease, ANN, BACE1, Descriptor, QSAR; SVM.

Campus : Amritapuri, Kochi

School : Center for Nanosciences

Center : Amrita Center for Nanosciences and Molecular Medicine Move

Department : Nanosciences and Molecular Medicine

Year : 2021

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 = 0.8688, q = 0.8600) and BACE1 (R = 0.8177, q = 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 = 0.8469, r = 0.8138) and BACE1 (r = 0.7805, r = 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.

Cite this Research Publication : Ganesan Dhamodharan and Dr. Gopi Mohan C., “Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.”, Molecular Diversity (Article in Press), 2021.

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