Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs
This work addresses molecular property prediction for drug discovery, presenting an incremental improvement by combining existing methods in a novel way.
The paper tackles molecular graph representation learning by integrating embodied and symbolic representations, using contrastive self-supervised learning with chemical knowledge graphs to enhance molecular graphs, resulting in improved performance on benchmark datasets.
Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss the use of dual embodied-symbolic concept representations for molecular graph representation learning, specifically with exemplar-based contrastive self-supervised learning (SSL). The embodied representations are learned from molecular graphs, and the symbolic representations are learned from the corresponding Chemical knowledge graph (KG). We use the Chemical KG to enhance molecular graphs with symbolic (semantic) knowledge and generate their augmented molecular graphs. We treat a molecular graph and its semantically augmented molecular graph as exemplars of the same semantic class, and use the pairs as positive pairs in exemplar-based contrastive SSL.