MoleMCL: a multi-level contrastive learning framework for molecular pre-training.

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 contrastiv

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11001485 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature