Prediction of Molecular Electronic Transitions Using Random Forests
Beomchang Kang, Chaok Seok, Juyong Lee
Abstract
Fluorescent molecules, fluorophores or dyes, play essential roles in bioimaging. Effective bioimaging requires fluorophores with diverse colors and high quantum yields for better resolution. An essential computational component to design novel dye molecules is an accurate model that predicts the electronic properties of molecules. Here, we present statistical machines that predict the excitation energies and associated oscillator strengths of a given molecule using the random forest algorithm. The excitation energies and oscillator strengths of a molecule are closely related to the emission spectrum and the quantum yields of fluorophores, respectively. In this study, we identified specific molecular substructures that induce high oscillator strengths of molecules. The results of our study are expected to serve as new design principles for designing novel fluorophores.