ASIA unversity:Item 310904400/112364
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    题名: Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer’s Disease
    作者: Chen, Hsin-Yi;Chen, Hsin-Yi;Jian-Qiang, C;Chen, Jian-Qiang;Li, Jun-Yan;Li, Jun-Yan;Hua, Hung-Jin;Huang, Hung-Jin;Chen, Xi;Chen, Xi;Zha, Hao-Ying;Zhang, Hao-Ying;陳語謙
    贡献者: 生物資訊與醫學工程學系
    日期: 2019-03
    上传时间: 2019-11-08 10:24:39 (UTC+8)
    摘要: It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer’s disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM candidates that can dock well with multiple targets. Support vector machine (SVM) and multiple linear regression (MLR) methods were utilized to obtain predicted models. In particular, the deep learning method and the random forest (RF) algorithm were adopted. We achieved R2 values of 0.927 on the training set and 0.862 on the test set with deep learning and 0.869 on the training set and 0.890 on the test set with RF. Besides, comparative molecular similarity indices analysis (CoMSIA) was performed to get a predicted model. All of the training models achieved good results on the test set. The stability of GSK3β protein–ligand complexes was evaluated using 100 ns of MD simulation. Methyl 3-O-feruloylquinate and cynanogenin A induced both more compactness to the GSK3β complex and stable conditions at all simulation times, and the GSK3β complex also had no substantial fluctuations after a simulation time of 5 ns. For TCM molecules, we used the trained models to calculate predicted bioactivity values, and the optimum TCM candidates were obtained by ranking the predicted values. The results showed that methyl 3-O-feruloylquinate contained in Phellodendron amurense and cynanogenin A contained in Cynanchum atratum are capable of forming stable interactions with GSK3β.
    關聯: Journal of Chemical Information and Modeling
    显示于类别:[生物資訊與醫學工程學系 ] 期刊論文




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