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Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease

机译：基于深度学习的目标筛选和相似性搜索帕金森病中途径的预测抑制剂

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摘要

Herein, a two-step de novo approach was developed for the prediction of piperine targets and another prediction of similar (piperine) compounds from a small molecule library using a deep-learning method. Deep-learning and neural-network approaches were used for target prediction, similarity searches, and validation. The present approach was trained on records containing the data. The model attained an overall accuracy of around 87.5%, where the training and test set was kept as 70% and 30% (17226/40197), respectively. This method predicted two targets (MAO-A and MAO-B) and 101 compounds as piperine derivatives. MAO-A and MAO-B are important drug targets in Parkinson's disease. Validation of this method was also performed by considering piperine and its targets (monoamine oxidase A and B) using molecular docking, dynamics simulation and post-simulation analysis of all the selected compounds. Rasagiline, lazabemide, and selegiline were selected as controls, which are already FDA-approved drugs against these targets. Molecular docking studies of the FDA-approved drugs and the compounds we predicted using DL and neural networks were carried out against MAO-A and MAO-B. Using the molecular docking's scoring function, molecular dynamics simulation and free energy calculations as extended validation methods, it was observed that the compounds predicted herein possessed excellent inhibitory effects against the selected targets. Thus, deep learning may play a very effective role in predicting the potential compounds, their targets and can play an expanded role in computer-aided drug approaches.
机译：在本文中，两个步骤的从头方法被用于的胡椒碱目标的预测和由小分子文库是使用深学习方法相似（胡椒碱）的化合物的另一预测显影。深学习，并用于目标预测，相似性搜索和验证的神经网络方法。本办法进行训练上包含的数据记录。该模型分别达到约87.5％，其中，所述训练和测试集合保持为70％和30％（40197分之17226）的总体精确度，。该方法预测的两个靶（MAO-A和MAO-B）和101种化合物作为胡椒碱衍生物。 MAO-A和MAO-B是帕金森氏症的重要药物靶点。此方法的验证还通过使用分子对接，动力学模拟和所有的选择的化合物的后仿真分析胡椒碱考虑其目标（单胺氧化酶A和B）进行。雷沙吉兰，lazabemide和司来吉兰作为对照组，这已经是美国FDA批准的药物对这些目标。美国食品药品管理局批准的药物，我们使用DL预测化合物和神经网络的分子对接研究，对MAO-A和MAO-B进行。使用分子对接的评分函数，分子动力学模拟和自由能的计算，扩展验证方法中，观察到的化合物本文预测具有针对选定的目标优良抑制作用。因此，深度学习可以在预测潜力的化合物，它们的目标起到非常有效的作用，并能起到计算机辅助药物的办法发挥更大的作用。

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  • 来源
    《RSC Advances》 |2019年第18期|共14页
  • 作者

    Khan Abbas; Kaushik Aman Chandra; Ali Syed Shujait; Ahmad Nisar; Wei Dong-Qing;

  • 作者单位

    Shanghai Jiao Tong Univ Sch Life Sci &

    Biotechnol Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Sch Life Sci &

    Biotechnol Shanghai 200240 Peoples R China;

    Univ Swat Ctr Biotechnol &

    Microbiol Swat Pakistan;

    Univ Swat Ctr Biotechnol &

    Microbiol Swat Pakistan;

    Shanghai Jiao Tong Univ Sch Life Sci &

    Biotechnol Shanghai 200240 Peoples R China;

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  • 正文语种 eng
  • 中图分类 化学;
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