Knowledge enhanced and guided graph contrastive learning for molecular property prediction

Molecular property prediction (MPP) lies at the core of fundamental tasks for AI-aided drug discovery. Recent studies have shown great promise in applying self-supervised learning (SSL) to cope with the data scarcity in MPP, and contrastive learning is one of the mainstream methods in SSL. However, current molecular graph contrastive learning methods suffer from two main challenges: molecular graph augmentation that preserves the molecular chemical semantics, and contrastive goal that captures the precise prior knowledge. To address these issues, we propose the Knowledge Enhanced and Guided Graph Contrastive Learning (KEGGCL). KEGGCL adopts the chemical element domain knowledge to generate two enhanced molecular graphs without changing the molecular chemical structure, ensuring the preservation of the molecular semantics and structure. Next, KEGGCL uses the quantitative estimate of drug-likeness as the guideline to push away sample pairs constituted of different molecules discriminately, capturing the precise prior knowledge. Then, KEGGCL utilizes the well-trained encoders on the featured molecular graph and two element knowledge enhanced molecular graphs to decide the final prediction jointly. Experiments on the 10 benchmark datasets from MoleculeNet show the superiority of KEGGCL. It provides a new graph contrastive manner to learn the precise prior knowledge for better predicting molecular property.