R2eGIN:残差重建增强图同构网络,用于准确预测聚adp核糖聚合酶抑制剂。

IF 2.4 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2025-08-29 eCollection Date: 2025-01-01 DOI:10.1177/11779322251366087
Candra Zonyfar, Soualihou Ngnamsie Njimbouom, Sophia Mosalla, Jeong-Dong Kim
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引用次数: 0

摘要

一种先进的图神经网络(GNN)在预测聚二磷酸核糖聚合酶抑制剂(PARPi)方面具有很大的前景。不幸的是,最近的研究利用图表示和分子描述符表示来设计模型,但在全面捕获原子之间的空间关系和上下文信息方面仍然面临挑战。此外,将分子描述符与图表示相结合可能会引入信息冗余或导致固有分子结构的丢失。为此,我们提出了一种新的残差重构增强图同构网络(R2eGIN)学习模型。具体来说,我们首先设计了一个残差GIN来学习分子表征,减少梯度消失的影响,并使模型能够捕获远程依赖关系。然后,通过预测邻接矩阵和节点特征,采用重构块对输入图进行重构。为了证明该模型的有效性,我们在PARPi的4个数据集上进行了大量的实验,并与现有的7个模型进行了比较。我们使用4个PARPi数据集对R2eGIN进行了评估,结果表明,所提出的模型与其他最先进的PARPi预测模型相当,甚至优于其他最先进的模型。此外,R2eGIN可以通过大幅减少传统药物开发方法中常见的时间和成本,彻底改变药物再利用过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.

R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.

R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.

R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.

An advanced graph neural network (GNN) is of great promise to facilitate predicting Poly ADPribose polymerase inhibitors (PARPi). Recent studies design models by leveraging graph representations and molecular descriptor representations, unfortunately, still face challenges in comprehensively capturing spatial relationships and contextual information between atoms. Moreover, combining molecular descriptors with graph representations may introduce information redundancy or lead to the loss of intrinsic molecular structures. To this end, we proposed a novel Residual Reconstruction Enhanced Graph Isomorphism Network (R2eGIN) learning model. Specifically, we first designed a residual GIN to learn molecular representations, reduced the impact of vanishing gradients, and enabled the model to capture long-range dependencies. Then, the reconstruction block, by predicting adjacency matrices and node features, was adopted to reconstruct the input graph. To prove the effectiveness of the proposed model, extensive experiments were conducted on 4 data sets of PARPi and compared with 7 existing models. Our evaluation of R2eGIN, conducted using 4 PARPi data sets, shows that the proposed model is comparable to or even outperforms other state-of-the-art models for PARPi prediction. Furthermore, R2eGIN can revolutionize the drug repurposing process through a substantial reduction in the time and costs commonly encountered in traditional drug development methods.

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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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