MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

IF 4.4 1区 生物学 Q1 BIOLOGY
Xin Zeng, Kai-Yang Zhong, Pei-Yan Meng, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen, Yi Li
{"title":"MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.","authors":"Xin Zeng, Kai-Yang Zhong, Pei-Yan Meng, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen, Yi Li","doi":"10.1186/s12915-024-01981-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.</p><p><strong>Results: </strong>We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA.</p><p><strong>Conclusions: </strong>During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346193/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12915-024-01981-3","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.

Results: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA.

Conclusions: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

MvGraphDTA:基于多视图的图深度模型,通过引入图和线图进行药物靶点亲和力预测。
背景:准确鉴定药物靶点亲和力(DTA)在制药业的药物筛选、设计和再利用中起着举足轻重的作用。它不仅能减少与生物实验相关的时间、人力和经济成本,还能加快药物开发进程。然而,DTA 识别方法要达到理想的计算精度水平仍是一项重大挑战:我们提出了一种新颖的基于多视图的图深度模型,即用于 DTA 预测的 MvGraphDTA。MvGraphDTA 利用图卷积网络(GCN)分别从药物和靶点的原始图中提取结构特征。它还进一步根据药物和靶标的原始图构建了以边缘为顶点的线图。GCN 还用于提取线图中的关系特征。为了增强从原始图和线图中提取的特征之间的互补性,MvGraphDTA 分别对提取的药物和目标的多视图特征进行了融合。最后,这些融合的特征被串联起来,并通过全连接(FC)网络来预测 DTA:在实验过程中,我们对所有使用的训练集进行了数据增强。实验结果表明,在 DTA 预测基准数据集上,MvGraphDTA 的表现优于最先进的竞争方法。此外,我们还在其他数据集上评估了 MvGraphDTA 的通用性和泛化性能。实验结果表明,MvGraphDTA 具有良好的通用性和泛化能力,是药物靶点相互作用预测的可靠工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BMC Biology
BMC Biology 生物-生物学
CiteScore
7.80
自引率
1.90%
发文量
260
审稿时长
3 months
期刊介绍: BMC Biology is a broad scope journal covering all areas of biology. Our content includes research articles, new methods and tools. BMC Biology also publishes reviews, Q&A, and commentaries.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信