Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms

Abstract

Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3b (C3b) and Protein Kinase R-like Endoplasmic Reticulum Kinase (PERK), have been demonstrated to be involved in pathogenesis pathways for Alzheimer's disease (AD) and considered as potential treatment targets to AD. Based on the concept of multitargets, a network pharmacology-based approach was employed to investigate potential Traditional Chinese Medicine (TCM) candidates that can dock well with GSNOR, C3b, Factor D and PERK proteins. To predict the bioactivities of candidates, Artificial Intelligence (AI) algorithms composed of seven machine learning algorithms and a deep learning model were performed to validate the docking results. Furthermore, in this study, we propose a novel combined method for efficiently exploring the predicted results of AI algorithms. Besides, Comparative force field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were performed to construct predicted models. The results show that the square correlation coefficients (R2) of all models are almost higher than 0.75, which also acquire good achievements on the test set. Moreover, the binding stability of the potential inhibitors were evaluated using 100 ns of MD simulation. Collectively, this study elucidate that the herbs Ardisia japonica, Ligusticum chuanxiong, Lippia nodiflora and Mirabilis jalapa containing 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), Glyasperin B, Nodifloridin A, Miraxanthin III and l-Valine-l-valine anhydride might be a potential medicine formula for AD.

Keywords

• Alzheimer’s disease
• Artificial intelligence
• Virtual screening
• Quantitative structure–activity relationship
• Molecular dynamic simulation