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Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease
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作者 Tong Wu Ruimei Lin +3 位作者 pengdi cui Jie Yong Heshui Yu Zheng Li 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2024年第10期1514-1526,共13页
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 pr... 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,includingα-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglialβ-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. 展开更多
关键词 Alzheimer's disease Deep learning models Drug screening Kaixinsan formula ACETYLCHOLINESTERASE Monoamine oxidase-A
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Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses 被引量:6
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作者 Shilin Chen Zheng Li +7 位作者 Sanyin Zhang Yuxin Zhou Xiaohe Xiao pengdi cui Binjie Xu Qinghe Zhao Shasha Kong Yuntao Dai 《Acta Pharmaceutica Sinica B》 SCIE CAS CSCD 2022年第11期4075-4097,共23页
Pharmaceutical analysis is a discipline based on chemical, physical, biological, and information technologies. At present, biotechnological analysis is a short branch in pharmaceutical analysis;however, bioanalysis is... Pharmaceutical analysis is a discipline based on chemical, physical, biological, and information technologies. At present, biotechnological analysis is a short branch in pharmaceutical analysis;however, bioanalysis is the basis and an important part of medicine. Biotechnological approaches can provide information on biological activity and even clinical efficacy and safety, which are important characteristics of drug quality. Because of their advantages in reflecting the overall biological effects or functions of drugs and providing visual and intuitive results, some biotechnological analysis methods have been gradually applied to pharmaceutical analysis from raw material to manufacturing and final product analysis,including DNA super-barcoding, DNA-based rapid detection, multiplex ligation-dependent probe amplification, hyperspectral imaging combined with artificial intelligence, 3D biologically printed organoids,omics-based artificial intelligence, microfluidic chips, organ-on-a-chip, signal transduction pathwayrelated reporter gene assays, and the zebrafish thrombosis model. The applications of these emerging biotechniques in pharmaceutical analysis have been discussed in this review. 展开更多
关键词 BIOTECHNOLOGY Pharmaceutical analysis Rawmaterials Manufacturing control Quality analysis
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