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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Knowledge graph information bottleneck enhanced molecular representation learning.

Jiaxin Dai¹, Dongmei Fu¹, Zhongwei Qiu²

¹Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.

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|May 17, 2025

Summary

This summary is machine-generated.

This study introduces a knowledge graph information bottleneck (KGIB) framework to improve molecular representation learning (MRL) for property prediction. KGIB effectively compresses knowledge, reducing noise and enhancing accuracy in molecular tasks.

Keywords:

Graph neural network Information bottleneck Knowledge graph Molecular property prediction Molecular representation learning

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Area of Science:

Computational Chemistry
Machine Learning
Cheminformatics

Background:

Molecular representation learning (MRL) is crucial for predicting molecular properties.
Graph-based MRL methods leverage molecular topology.
Integrating knowledge graphs (KGs) enhances MRL but can introduce noise.

Purpose of the Study:

To develop a novel framework for enhanced molecular representation learning.
To address the issue of redundant and noisy information from external knowledge sources.
To improve the minimal sufficiency of knowledge used in MRL.

Main Methods:

Proposed a knowledge graph information bottleneck (KGIB) framework.
Constructed a molecular knowledge graph (MKG) detailing molecule-functional group-element relationships.
Implemented a knowledge compression module to identify minimal sufficient knowledge subgraphs.
Developed a knowledge alignment module to capture topology-semantic dependencies.

Main Results:

KGIB framework demonstrated superior performance across ten real-world tasks.
Outperformed state-of-the-art baselines on six tasks.
Achieved highly competitive results on the remaining four tasks.
Effectively minimized irrelevant knowledge noise while preserving essential information.

Conclusions:

The proposed KGIB framework significantly enhances molecular representation learning.
Effective knowledge compression and alignment are key to improving MRL performance.
KGIB offers a superior approach for molecular property prediction by optimizing knowledge integration.