Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer&#39;s disease

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

doi:10.1016/j.jpha.2024.101022

Volume 14 Issue 10

Oct. 2024

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Tong Wu, Ruimei Lin, Pengdi Cui, Jie Yong, Heshui Yu, Zheng Li. Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 101022. doi: 10.1016/j.jpha.2024.101022

Citation:

Tong Wu, Ruimei Lin, Pengdi Cui, Jie Yong, Heshui Yu, Zheng Li. Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 101022. doi: 10.1016/j.jpha.2024.101022

Citation:

Tong Wu, Ruimei Lin, Pengdi Cui, Jie Yong, Heshui Yu, Zheng Li. Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 101022. doi: 10.1016/j.jpha.2024.101022

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

doi: 10.1016/j.jpha.2024.101022

Tong Wu^a,
Ruimei Lin^a,
Pengdi Cui^a,
Jie Yong^a,
Heshui Yu^{a,b,c
,
,},
Zheng Li^{a,b,c,d
,
,}

a. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China;
b. State Key Laboratory of Component-Based Chinese Medicine, Tianjin, 301617, China;
c. Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China;
d. National Key Laboratory of Chinese Medicine Modernization, Tianjin, 301617, China

Funds:

This work was supported by the Science and Technology Project of Haihe Laboratory of Modern Chinese Medicine, China (Grant No.:22HHZYSS00003) and the Science and Technology Program of Tianjin, China (Grant No.:22ZYJDSS00100). The authors would like to thank the support fromInnovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine, China (Grant No:ZYYCXTD-D-202002).

Received Date: Dec. 14, 2023
Accepted Date: Jun. 11, 2024
Rev Recd Date: May 29, 2024
Publish Date: Jun. 13, 2024

Abstract

Abstract

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 compounds 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-butylphenol 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 developing AD therapies and provide a strong platform for AD drug discovery.
- Alzheimer's disease,
- Deep learning models,
- Drug screening,
- Kaixinsan formula,
- Acetylcholinesterase,
- Monoamine oxidase-A

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References(43)

References

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