Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease

扫码查看

原文链接

国家科技期刊平台
NETL
NSTL
万方数据

外文摘要：Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic com-pounds for AD from traditional Chinese medicine formulas with strong empirical support.We developed four deep neural network(DNN)models for AD drugs screening at the disease and target levels.The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level,while the acetylcholinesterase(AChE),monoamine oxidase-A(MAO-A),and 5-hydroxytryptamine 6(5-HT6)models were trained for specific AD targets.All four models performed excellently and were used to identify potential AD agents in the Kaixinsan(KXS)formula.High-scoring compounds underwent experimental validation at the enzyme,cellular,and animal levels.Compounds like 2,4-di-tert-butyl-phenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A.Additionally,13 compounds,including α-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglial β-amyloid phagocytosis,aiding in clearing AD pathological substances.Our results demonstrate the effectiveness of deep learning models in devel-oping AD therapies and provide a strong platform for AD drug discovery.

外文关键词：

Alzheimer's diseaseDeep learning modelsDrug screeningKaixinsan formulaAcetylcholinesteraseMonoamine oxidase-A

作者：

Tong Wu、Ruimei Lin、Pengdi Cui、Jie Yong、Heshui Yu、Zheng Li

展开 >

作者单位：

College of Pharmaceutical Engineering of Traditional Chinese Medicine,Tianjin University of Traditional Chinese Medicine,Tianjin 301617,China

College of Pharmaceutical Engineering of Traditional Chinese Medicine,Tianjin University of Traditional Chinese Medicine,Tianjin,301617,China

State Key Laboratory of Component-Based Chinese Medicine,Tianjin,301617,China

Haihe Laboratory of Modern Chinese Medicine,Tianjin,301617,China

National Key Laboratory of Chinese Medicine Modernization,Tianjin,301617,China

展开 >

出版年：

2024

DOI：

10.1016/j.jpha.2024.101022

药物分析学报(英文)

西安交通大学

药物分析学报(英文)

影响因子：0.244

ISSN：2095-1779

年,卷(期)：2024.14(10)