Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

TL;DR

DIG-Mol is a novel self-supervised graph neural network that uses contrastive learning and dual interactions to improve molecular property prediction, especially with limited labeled data, achieving state-of-the-art results.

Contribution

Introduces DIG-Mol, a self-supervised GNN framework with contrastive learning and dual interactions, enhancing molecular representation and prediction accuracy.

Findings

State-of-the-art performance on molecular property tasks

Improved transferability with limited labeled data

Enhanced interpretability and molecular understanding

Abstract

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 enhanced interpretability and representation capabilities. These findings confirm the effectiveness of our approach in overcoming challenges faced by traditional methods and mark a significant advance in molecular property prediction.