School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.
School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
Molecules. 2022 Jul 26;27(15):4780. doi: 10.3390/molecules27154780.
Parkinson's disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson's disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision-recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.
帕金森病（PD）是一种严重的神经退行性疾病。目前大多数治疗方法只能缓解症状，而不能阻止疾病的进展。因此，寻找能够彻底治愈 PD 的药物至关重要。通过药物重定位寻找现有药物的新适应症，不仅可以降低风险和成本，还可以提高研究和开发效率。提出了一种基于多源数据集成和卷积神经网络的药物重定位方法，以识别潜在的与帕金森病相关的药物。使用多源数据构建相似性网络，并利用拓扑信息对药物和 PD 相关蛋白进行特征化。然后，采用扩散成分分析方法来降低特征维度。最后，构建了一个卷积神经网络模型来识别现有药物与 LProts（PD 相关蛋白）之间的潜在关联。基于 10 折交叉验证，所开发的方法在准确性、特异性、敏感性、马修斯相关系数、接收者操作特征曲线下面积和精度-召回曲线下面积方面的表现分别达到了 91.57%、87.24%、95.27%、0.8304、0.9731 和 0.9727。与现有方法相比，该方法在敏感性、准确性、稳健性等方面具有优势。此外，通过分子对接预测的一些潜在的 PD 治疗药物进一步证明，它们可以通过作用于 PD 的已知靶点发挥疗效，可能是进一步实验研究的潜在 PD 治疗药物。预计该方法可以作为药物重定位和病理机制研究的有力工具。
