Can I read Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction on EtoBox?

Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction by Zhao, Zexing; Shi, Guangsi; Wu, Xiaopeng; Ren, Ruohua; Gao, Xiaojun; Li, Fuyi is a scholarly article available to read on EtoBox.

What is Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction about?

Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenges such as limited ability to generalize, and inadequate representation of learning from unlabeled data, especially for tasks specific to molecular structures. To address these limitations, we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. This architecture leverages the power of contrast learning with dual interaction mechanisms and unique molecular graph enhancement strategies. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. The framework's ability to extract key information about molecular structure and higher-order semantics is supported by minimizing loss of contrast. We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks. In addition to demonstrating superior transferability in a small number of learning scenarios, our visualizations highlight DIG-Mol's enha

Author

Zhao, Zexing; Shi, Guangsi; Wu, Xiaopeng; Ren, Ruohua; Gao, Xiaojun; Li, Fuyi

Published

2024

Language

EN