Han Xingxing, Xie Xiaoxia, Zhao Ranran, Li Yu, Ma Pengzhen, Li Huan, Chen Fengming, Zhao Yufeng, Tang Zhishu
State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-Di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China.
National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China.
Chin Med. 2024 Sep 11;19(1):124. doi: 10.1186/s13020-024-00994-y.
Drug repositioning has the potential to reduce costs and accelerate the rate of drug development, with highly promising applications. Currently, the development of artificial intelligence has provided the field with fast and efficient computing power. Nevertheless, the repositioning of traditional Chinese medicine (TCM) is still in its infancy, and the establishment of a reasonable and effective research method is a pressing issue that requires urgent attention. The use of graph neural network (GNN) to compute the similarity between TCM prescriptions to develop a method for finding their new indications is an innovative attempt.
This paper focused on traditional Chinese medicine prescriptions containing ephedra, with 20 prescriptions for treating external cough and asthma taken as target prescriptions. The remaining 67 prescriptions containing ephedra were taken as to-be-matched prescriptions. Furthermore, a multitude of data pertaining to the prescriptions, including diseases, disease targets, symptoms, and various types of information on herbs, was gathered from a diverse array of literature sources, such as Chinese medicine databases. Then, cosine similarity and Jaccard coefficient were calculated to characterize the similarity between prescriptions using graph convolutional network (GCN) with a self-supervised learning method, such as deep graph infomax (DGI).
A total of 1340 values were obtained for each of the two calculation indicators. A total of 68 prescription pairs were identified after screening with 0.77 as the threshold for cosine similarity. Following the removal of false positive results, 12 prescription pairs were deemed to have further research value. A total of 5 prescription pairs were screened using a threshold of 0.50 for the Jaccard coefficient. However, the specific results did not exhibit significant value for further use, which may be attributed to the excessive variety of information in the dataset.
The proposed method can provide reference for finding new indications of target prescriptions by quantifying the similarity between prescriptions. It is expected to offer new insights for developing a scientific and systematic research methodology for traditional Chinese medicine repositioning.
药物重新定位具有降低成本和加速药物开发速度的潜力，应用前景广阔。目前，人工智能的发展为该领域提供了快速高效的计算能力。然而，中药的重新定位仍处于起步阶段，建立合理有效的研究方法是一个亟待关注的紧迫问题。利用图神经网络（GNN）计算中药方剂之间的相似度以开发寻找其新适应症的方法是一种创新尝试。
本文聚焦于含麻黄的中药方剂，选取20首治疗外感咳嗽和哮喘的方剂作为目标方剂。其余67首含麻黄的方剂作为待匹配方剂。此外，从中医数据库等多种文献来源收集了与方剂相关的大量数据，包括疾病、疾病靶点、症状以及各种草药信息。然后，使用图卷积网络（GCN）和深度图信息最大化（DGI）等自监督学习方法计算余弦相似度和杰卡德系数，以表征方剂之间的相似度。
两个计算指标各自共获得1340个值。以0.77作为余弦相似度阈值进行筛选后，共识别出68对方剂。去除假阳性结果后，有12对方剂被认为具有进一步研究价值。以0.50作为杰卡德系数阈值共筛选出5对方剂。然而，具体结果在进一步使用中未显示出显著价值，这可能归因于数据集中信息种类过多。
所提出的方法可为通过量化方剂之间的相似度来寻找目标方剂的新适应症提供参考。有望为开发科学系统的中药重新定位研究方法提供新见解。
