Dataset Information
MoleMCL: a multi-level contrastive learning framework for molecular pre-training.
Ontology highlight
ABSTRACT: Motivation
Molecular 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.Results
Experimental 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.
SUBMITTER: Zhang X
PROVIDER: S-EPMC11001485 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
Publications
MoleMCL: a multi-level contrastive learning framework for molecular pre-training.
Bioinformatics (Oxford, England) 20240301 4
<h4>Motivation</h4>Molecular 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 prop ...[more]