Chemical Graph

Chemical graphs represent molecules as networks of atoms and bonds, enabling the application of graph theory and machine learning to predict molecular properties and design new compounds. Current research focuses on developing sophisticated graph neural networks (GNNs), often incorporating contrastive learning and self-supervised techniques, to improve the accuracy and efficiency of molecular property prediction. These advancements are significantly impacting drug discovery and materials science by accelerating the identification and design of molecules with desired characteristics, such as improved drug efficacy or enhanced catalytic activity. Furthermore, research is actively exploring methods to enhance the interpretability of GNN models and to efficiently handle the large datasets involved in training these models.