DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

药物基因组学 计算机科学 药品 计算生物学 水准点(测量) 药物反应 图形 药物发现 人工智能 机器学习 数据挖掘 生物信息学 生物 药理学 理论计算机科学 地理 大地测量学
作者
Jiyeon Han,Min Ji Kang,Sanghyuk Lee
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:174: 108436-108436 被引量:2
标识
DOI:10.1016/j.compbiomed.2024.108436
摘要

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (Drug Synergy Prediction by Integrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.
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