QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.

[1]  K. Camphausen,et al.  Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics , 2022, International journal of molecular sciences.

[2]  D. Fairlie,et al.  QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites. , 2022, ACS infectious diseases.

[3]  Yongzhen Peng,et al.  Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review , 2021, International journal of molecular sciences.

[4]  Irvanizam Irvanizam,et al.  Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index , 2021 .

[5]  A. Yan,et al.  SAR and QSAR research on tyrosinase inhibitors using machine learning methods , 2021, SAR and QSAR in environmental research.

[6]  Yu Kang,et al.  Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets , 2020, Briefings Bioinform..

[7]  Piyali Mukherjee,et al.  Flavonoids as BACE1 inhibitors: QSAR modelling, screening and in vitro evaluation. , 2020, International journal of biological macromolecules.

[8]  I. Kurniawan,et al.  Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent , 2020, SAR and QSAR in environmental research.

[9]  Artem Cherkasov,et al.  QSAR without borders. , 2020, Chemical Society reviews.

[10]  Gideon Adamu Shallangwa,et al.  In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype , 2020, Beni-Suef University Journal of Basic and Applied Sciences.

[11]  T. Al‐Tel,et al.  BACE1 inhibitors: Current status and future directions in treating Alzheimer's disease , 2020, Medicinal research reviews.

[12]  D. Seripa,et al.  Serum beta-secretase 1 (BACE1) activity as candidate biomarker for late-onset Alzheimer’s disease , 2019, GeroScience.

[13]  Sungroh Yoon,et al.  Comprehensive ensemble in QSAR prediction for drug discovery , 2019, BMC Bioinformatics.

[14]  Maria Vanina Martinez,et al.  QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease , 2019, Scientific Reports.

[15]  R. Dhanalakshmi,et al.  Stability of feature selection algorithm: A review , 2019, J. King Saud Univ. Comput. Inf. Sci..

[16]  Tatsuya Takagi,et al.  Mordred: a molecular descriptor calculator , 2018, Journal of Cheminformatics.

[17]  Virapong Prachayasittikul,et al.  Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking , 2016, PeerJ.

[18]  Ashok Sharma,et al.  Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis , 2016, Journal of biomolecular structure & dynamics.

[19]  R. Vassar BACE1 inhibitor drugs in clinical trials for Alzheimer’s disease , 2014, Alzheimer's Research & Therapy.

[20]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[21]  Ying Cao,et al.  Advance and Prospects of AdaBoost Algorithm , 2013, Acta Automatica Sinica.

[22]  Bieke Dejaegher,et al.  Feature selection methods in QSAR studies. , 2012, Journal of AOAC International.

[23]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[24]  Roberto Todeschini,et al.  Impact of Molecular Descriptors on Computational Models. , 2018, Methods in molecular biology.