Machine Learning Prediction of Optical Properties of Coumarin Derivatives Using Gaussian-Weighted Graph Convolution and Subgraph Modular Input

Coumarin derivatives have been widely developed and utilized as chromophores and fluorophores in various research fields. In this study, we constructed an experimental database of the optical propertiesspecifically, absorption and emission wavelengths measured in solutionsand developed a machine learning (ML) model based on Gaussian-weighted graph convolution (GWGC) and subgraph modular input (SMI) to predict these properties. The GWGC was introduced as a novel molecular representation that accounts for interatomic effects among neighboring atoms when the optical properties of coumarin derivatives were predicted. The SMI was introduced to represent coumarin derivatives as subgraphs composed of a coumarin core and six substituents, thereby modularizing the molecular vector into a core vector and substituent vectors. This approach encodes both the separate chemical information on the core and substituents as well as the positional information on the substituents, facilitating an understanding of how each substituent influences the optical properties of the coumarin core. ML models leveraging GWGC and SMI outperformed those based on RDKit descriptors and count-based Morgan fingerprint. The ML models with GWGC and SMI can be generally applied to predict properties of molecules composed of a core structure and its various substituents.