Prototype-based contrastive substructure identification for molecular property prediction.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae565
Gaoqi He, Shun Liu, Zhuoran Liu, Changbo Wang, Kai Zhang, Honglin Li
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Abstract

Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https://github.com/VRPharmer/POSIT.

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用于分子特性预测的基于原型的对比子结构识别。
基于子结构的表征学习已成为对复杂属性图进行特征化的有力方法,在分子性质预测(MPP)方面取得了可喜的成果。然而,现有的 MPP 方法主要依赖人工定义的规则来提取子结构。如何从众多分子图中自适应地识别有意义的子结构,以适应 MPP 任务,仍然是一个有待解决的难题。为此,本文提出了基于原型的自监督子结构识别(Prototype-based cOntrastive Substructure IdentificaTion,POSIT)--一种自监督框架,用于自主发现分子图中的子结构原型,从而指导端到端的分子破碎。在预训练阶段,POSIT 强调子结构识别的两个关键方面:首先,它施加软连接性约束,鼓励生成拓扑上有意义的子结构;其次,它通过原型-子结构对比聚类目标,将生成的子结构与衍生原型对齐,确保聚类内基于属性的相似性。在微调阶段,设计了一种跨尺度关注机制,以整合子结构级信息,增强分子表征。POSIT 框架的有效性通过各种实际数据集的实验结果得到了证明,这些数据集涵盖了分类和回归任务。此外,可视化分析验证了化学先验与已识别子结构的一致性。源代码可通过 https://github.com/VRPharmer/POSIT 公开获取。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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