Li Jun-Yan, Chen Hsin-Yi, Dai Wen-Jie, Lv Qiu-Jie, Chen Calvin Yu-Chian
School of Intelligent Systems Engineering, Artificial Intelligence Medical Center, Sun Yat-sen University, Shenzhen 510275, China.
School of Pharmacy, Sun Yat-sen University, Shenzhen 510275, China.
J Phys Chem Lett. 2019 Sep 5;10(17):4947-4961. doi: 10.1021/acs.jpclett.9b02220. Epub 2019 Aug 14.
Longevity is a very important and interesting topic, and 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 , 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 , , and , which might be potent TCMs for two targets.
长寿是一个非常重要且有趣的话题,并且已被证明与长寿相关。我们结合网络药理学、机器学习、深度学习和分子动力学(MD)模拟来研究潜在的先导药物。将相关蛋白胰岛素样生长因子1受体(IGF1R)和胰岛素受体(IR)与中药数据库进行对接,以筛选出几种新的候选药物。此外,还执行了九种不同的机器学习算法来构建可靠且准确的预测模型。而且,我们使用新颖的深度学习算法来构建预测模型。所有这些模型都取得了显著成果,在训练集上均大于0.87,在测试集上分别高于0.88。还进行了长达500纳秒的分子动力学模拟,以验证蛋白质-配体的性质和稳定性。最后,我们获得了[具体药物名称1]、[具体药物名称2]和[具体药物名称3],它们可能是针对两个靶点的有效中药。