ArticleBioinformatics (Oxford, England)2024
MoleMCL: a multi-level contrastive learning framework for molecular pre-training.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- HYG-mol: An Interpretable Multimodal Hypergraph Framework for Molecular Property Prediction.Computational and structural biotechnology journal · 2026Article
- EncoderMap III: A Dimensionality Reduction Package for Feature Exploration in Molecular Simulations.Journal of chemical information and modeling · 2025Article
- MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data.Journal of cheminformatics · 2025Article
- GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.BMC biology · 2025Article
- GTAM: a molecular pretraining model with geometric triangle awareness.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
motivationMolecular representation learning plays an indispensable role in crucial tasks such as property prediction and drug design. Despite the notable achievements of molecular pre-training models, current methods often fail to capture both the structural and feature semantics of molecular graphs. Moreover, while graph contrastive learning has unveiled new prospects, existing augmentation techniques often struggle to retain their core semantics. To overcome these limitations, we propose a gradient-compensated encoder parameter perturbation approach, ensuring efficient and stable feature augmentation. By merging enhancement strategies grounded in attribute masking and parameter perturbation, we introduce MoleMCL, a new MOLEcular pre-training model based on multi-level contrastive learning.
resultsExperimental results demonstrate that MoleMCL adeptly dissects the structure and feature semantics of molecular graphs, surpassing current state-of-the-art models in molecular prediction tasks, paving a novel avenue for molecular modeling. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available in GitHub at https://github.com/BioSequenceAnalysis/MoleMCL.
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