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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112735

    Title: Artificial Intelligence Approach to Investigate the Longevity Drug
    Authors: Li, Jun-Yan;Li, Jun-Yan;Chen, Hsin-Yi;Chen, Hsin-Yi;Dai, Wen-jie;Dai, Wen-jie;Lv, Qiu-Jie;Lv, Qiu-Jie;陳語謙
    Contributors: 生物資訊與醫學工程學系
    Date: 2019-09
    Issue Date: 2020-08-27 14:44:11 (UTC+8)
    Publisher: 亞洲大學
    Abstract: Longevity is a very important and interesting topic, and Klotho has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Related protein insulin-like growth factor 1 receptor (IGF1R) and insulin receptor (IR) were docked with the traditional Chinese medicine (TCM) database to screen out several novel candidates. Besides, nine different machine learning algorithms were performed to build reliable and accurate predicted models. Moreover, we used the novel deep learning algorithm to build predicted models. All of these models obtained significant R2, which are all greater than 0.87 on the training set and higher than 0.88 for the test set, respectively. The long time 500 ns molecular dynamics simulation was also performed to verify protein–ligand properties and stability. Finally, we obtained Antifebrile Dichroa, Holarrhena antidysenterica, and Gelsemium sempervirens, which might be potent TCMs for two targets.
    Relation: Journal of Physical Chemistry Letters
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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