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November 30, 2025IEEE Journal of Biomedical and Health Informatics

A Novel Multi-Perspective Framework for Molecule Pretraining: From Atom to Motif Views

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Authors

WWWei WangDLDengzhen LuSDSuyu Dong

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Overview

A2M-Mol improves predictive accuracy in molecular property prediction, highlighting its potential to accelerate drug discovery.

Key Points

  • A2M-Mol enhances predictive accuracy for molecular properties, showing effectiveness in drug discovery.
  • Improvements were consistent across benchmarks and backbone architectures, demonstrating versatility.
  • This observational analysis employs a multi-perspective framework through parallel graph constructions.
  • The framework's ability to encode chemical knowledge suggests avenues for further research in molecular applications.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/692b943e1d383f2b2a378af3https://doi.org/10.1109/jbhi.2025.3637248
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1MPMFMol: Multitask Self-Supervised Pretraining with Multimodal Fine-Tuning for Molecular Property Prediction2026
  2. 2Self-supervised Pre-training via Multi-view Graph Information Bottleneck for Molecular Property Prediction2024 · 1 citations
  3. 3MMPCS: multi-view molecular pretraining based on consistency information and specific information2026
  4. 4Molecular Motif Learning as a pretraining objective for molecular property prediction2025 · 4 citations
  5. 5Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction2024 · 17 citations