面向药物发现的深度图学习
摘要：Deep graph learning (graph neural network) has received great attention
from artificial intelligence researchers in recent years. This report will introduce
the latest progress of deep graph learning, and its applications in drug repurposing. Specifically, we built a comprehensive knowledge graph that includes 15 million
edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed
publications. Using Amazon’s AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials.
深度图学习(图神经网络)近年来受到人工智能研究人员的广泛关注。
本报告将介绍深度图学习的最新进展及其在药物再利用方面的应用。
具体地说，我们建立了一个全面的知识图谱，其中包括1500万条边，跨越39种关系，连接药物、疾病、蛋白质/基因、途径和表达，这些关系来自于2400万PubMed出版物的大型科学库。
利用亚马逊的AWS计算资源和基于网络的深度学习框架，我们确定了41种可重复使用的药物(包括地塞米松、消炎痛、氯硝酰胺和托瑞米芬）通过sars - cov -2感染的人类细胞的转录组和蛋白质组学数据以及正在进行的临床试验的数据，验证了它们与COVID-19的治疗相关性。