计算处方之间的相似性,以根据图形神经网络找到它们的新指示
Xingxing Han1, Xiaoxia Xie2, Ranran Zhao1
1State 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.
Chinese medicine
|September 11, 2024
概括
本研究引入了一种新的图形神经网络 (GNN) 方法,通过测量相似性来识别传统中医 (TCM) 处方的新用途. 该方法成功地确定了TCM的潜在药物重新定位候选者,有助于系统研究.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 传统中国医药 传统中国医药
背景情况:
- 药物重新定位加速了药物开发,但在中国传统医学 (TCM) 中面临着挑战.
- 人工智能 (AI) 为药物开发提供计算能力.
- 开发用于TCM重新定位的有效研究方法至关重要.
研究的目的:
- 开发一种使用图形神经网络 (GNN) 进行TCM处方重新定位的创新方法.
- 计算TCM处方之间的相似性,以确定潜在的新适应症.
- 为TCM重新定位建立一个科学和系统的研究方法.
主要方法:
- 专注于用于咳和喘的含有ephedra的TCM处方.
- 从文献和数据库中收集了有关疾病,目标,症状和草药的各种数据.
- 使用图形卷积网络 (GCNs) 与深度图形信息 (DGI) 进行相似性计算 (共弦相似性,贾卡德系数).
主要成果:
- 确定了12对具有研究价值的处方配对,使用0.77.7的等号相似度值.
- 使用0.50的雅卡尔系数值选了5对处方药,尽管结果不那么显著.
- 同位数相似性方法在识别TCM重新定位候选人方面表现有前途.
结论:
- 拟议的基于GNN的方法量化了处方相似性,以指导新指示的发现.
- 这种方法为系统和科学的TCM重新定位研究提供了基础.
- 这些发现有助于推进传统中医药药物发现领域.
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