Wang Xun, Zhong Yue, Ding Mao
College of Computer Science and Technology, China University of Petroleum, Shandong, China.
Department of Neurology Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Front Genet. 2021 Feb 15;12:638330. doi: 10.3389/fgene.2021.638330. eCollection 2021.
Alzheimer's disease (AD) is a common neurodegenerative dementia in the elderly. Although there is no effective drug to treat AD, proteins associated with AD have been discovered in related studies. One of the proteins is mitochondrial fusion protein 2 (Mfn2), and its regulation presumably be related to AD. However, there is no specific drug for Mfn2 regulation. In this study, a three-tunnel deep neural network (3-Tunnel DNN) model is constructed and trained on the extended Davis dataset. In the prediction of drug-target binding affinity values, the accuracy of the model is up to 88.82% and the loss value is 0.172. By ranking the binding affinity values of 1,063 approved drugs and small molecular compounds in the DrugBank database, the top 15 drug molecules are recommended by the 3-Tunnel DNN model. After removing molecular weight <200 and topical drugs, a total of 11 drug molecules are selected for literature mining. The results show that six drugs have effect on AD, which are reported in references. Meanwhile, molecular docking experiments are implemented on the 11 drugs. The results show that all of the 11 drug molecules could dock with Mfn2 successfully, and 5 of them have great binding effect.
阿尔茨海默病（AD）是老年人中常见的神经退行性痴呆。尽管尚无治疗AD的有效药物，但在相关研究中已发现与AD相关的蛋白质。其中一种蛋白质是线粒体融合蛋白2（Mfn2），其调节可能与AD有关。然而，尚无针对Mfn2调节的特异性药物。在本研究中，构建了一个三通道深度神经网络（3-Tunnel DNN）模型，并在扩展的戴维斯数据集上进行训练。在预测药物-靶点结合亲和力值时，该模型的准确率高达88.82%，损失值为0.172。通过对DrugBank数据库中1063种已批准药物和小分子化合物的结合亲和力值进行排名，3-Tunnel DNN模型推荐了前15种药物分子。在去除分子量<200的分子和局部用药后，共选择11种药物分子进行文献挖掘。结果表明，有6种药物对AD有作用，这在参考文献中已有报道。同时，对这11种药物进行了分子对接实验。结果表明，所有11种药物分子均能成功与Mfn2对接，其中5种具有较强的结合作用。
