吡咯喹啉醌
神经保护
神经退行性变
神经科学
药物发现
药理学
奶油
化学
生物
医学
生物化学
疾病
转录因子
基因
病理
辅因子
酶
作者
Xinuo Li,Yuan Sun,Zheng Zhang,Jinran Li,Sai Liu,Long Chen,Yiting Shi,Min Wang,Zheying Zhu,Guangji Wang,Qiulun Lu
标识
DOI:10.1002/advs.202308970
摘要
Abstract Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood–brain barrier (BBB) permeability is predicted and the anti‐inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean‐DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂‐induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti‐neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.
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