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Related Experiment Video

Updated: Sep 23, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph.

Mengying Sun1, Jing Xing2, Huijun Wang3

  • 1Michigan State University, East Lansing, Michigan, USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|May 16, 2022
PubMed
Summary

This study introduces MoCL, a novel graph contrastive learning framework for biomedical applications. MoCL enhances molecular graph representation learning by incorporating domain knowledge for improved drug discovery tasks.

Keywords:
Contrastive LearningDomain knowledgeMolecular Graph

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Area of Science:

  • Biomedical informatics
  • Machine learning
  • Drug discovery

Background:

  • Graph neural networks (GNNs) are increasingly used in biomedicine for drug-related tasks.
  • Unsupervised pretraining of GNNs is crucial due to expensive labeling costs.
  • Existing graph contrastive learning methods have limitations in domain-specific augmentation and global structure consideration.

Purpose of the Study:

  • To develop a novel graph contrastive learning framework, MoCL, tailored for molecular graphs in the biomedical domain.
  • To address limitations of general graph augmentations and incorporate global dataset structure into representation learning.
  • To improve unsupervised pretraining of GNNs for drug-related applications.

Main Methods:

  • Proposed MoCL framework utilizing domain knowledge at local and global levels.
  • Local-level knowledge guides augmentation to preserve graph semantics.
  • Global-level knowledge encodes inter-graph similarity for richer representations.
  • Employed a double contrastive objective for learning.

Main Results:

  • MoCL achieved state-of-the-art performance on various molecular datasets.
  • Evaluated MoCL under linear and semi-supervised settings.
  • Demonstrated the effectiveness of domain-specific augmentations and global structure integration.

Conclusions:

  • MoCL offers a powerful approach for unsupervised representation learning of molecular graphs.
  • Incorporating domain knowledge significantly enhances GNN performance in biomedical tasks.
  • The framework shows promise for advancing drug discovery and development.