Molecular Property Prediction Based on Dual-Channel Feature Separation Network and Contrastive Learning

Contrastive learning, a prominent self-supervised technique, has shown great potential in molecular property prediction by reducing reliance on labeled data. However, existing methods face challenges such as disrupting molecular structures through aggressive graph augmentations and feature interference from extracting all features within a single network. To address these issues, this paper propose DCFS-CL (Dual-Channel Feature Separation with Contrastive Learning). First, a property-preserving augmentation strategy modifies non-essential structures while retaining key scaffolds. Then, a dual-channel network is introduced to separately extract inherent and auxiliary features, enhancing interpretability and task adaptability. As a result, DCFS-CL achieves outstanding accuracy and robustness across seven biological classification datasets and demonstrates high predictive performance on an electrochemical regression dataset. This framework offers strong generalization, making it well-suited for molecular screening and property prediction in data-scarce scenarios.