利用相互关注网络整合异质信息,预测药物与目标之间的相互作用。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhan, Aoyi Wang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li
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引用次数: 0

摘要

背景:鉴定药物与靶标的相互作用是药物发现不可或缺的一部分。虽然传统的浅层机器学习和最近基于药物和靶蛋白的化学基因组学特性的深度学习方法已将这种预测性能的提高推向了一个新的水平,但这些方法仍然难以适应新型结构。另外,大规模生物和药理学数据也为加速药物-靶标相互作用预测提供了新的途径。方法:在此,我们提出了DrugMAN,一种通过整合多重异构功能网络和相互关注网络(MAN)来预测药物-靶标相互作用的深度学习模型。DrugMAN采用基于图注意力网络的整合算法,通过整合在特定筛选条件下收集到的4个药物网络和7个基因/蛋白质网络,分别学习药物和靶标蛋白质的网络特异性低维特征。然后,DrugMAN通过相互注意网络捕捉药物和靶标表征之间的相互作用信息,从而改进药物-靶标预测:与基于化学信息的方法SVM、RF、DeepPurpose以及基于网络的深度学习方法DTINet和NeoDT相比,DrugMAN在四种不同场景下取得了最佳性能,尤其是在真实世界场景下。与 SVM、RF、Deepurpose、DTINet 和 NeoDT 相比,DrugMAN 在热启动到双冷场景下的 AUROC、AUPRC 和 F1-Score 下降幅度最小。这一结果归功于DrugMAN对异构数据的学习,表明DrugMAN具有良好的泛化能力。综上所述,DrugMAN 能够挖掘药物-靶点相互作用的异构信息,是药物发现和药物再利用的有力工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drug-target interaction prediction by integrating heterogeneous information with mutual attention network.

Background: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.

Methods: Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.

Results: DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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