Enhancing molecular property prediction of transformer models with dual graph representation (opens in new tab)

Accurate prediction of molecular properties is central to advancing chemistry, materials science, and drug discovery. Machine learning on molecular graphs depends critically on representations that capture the topology and structure of molecules. Here we propose the dual graph transformer (DGT), a self-attention architecture that jointly models atom and bond graphs to achieve comprehensive molecular encodings. DGT fuses atom and bond features, graph topology and structure, and stereogeometric...