Drug discovery and mechanism prediction with explainable graph neural networks - Nature

Summary

The article discusses using Graph Neural Networks (GNNs) for predicting drug responses, focusing on molecule representations like SMILES and structural formulas. It relates to chemical nomenclature using node and edge features to encode molecular structures, including hydrogen atoms within graphs. Tags like "Chemical nomenclature" and "Simplified Molecular Input Line Entry System" are relevant to converting drug data for GNNs, exemplified by the use of SMILES for encoding molecules into computational graphs.