Prediction of Molecular Electronic Transitions Using Random Forests

Fluorescent molecules, fluorophores, play essential roles in bioimaging. Attachment of fluorophores to proteins enables observation of the detailed structure and dynamics of biological reactions occurring in the cell. Effective bioimaging requires fluorophores with high quantum yields to detect weak signals. Besides, fluorophores with various emission frequencies are necessary to extract richer information. An essential com- putational component to discover novel functional molecules is to predict molecular properties. Here, we present statistical machines that predict excitation energies and associated oscillator strengths of a given molecule using a random forest algorithm. Ex- citation energies and oscillator strengths are directly related to the emission spectrum and the quantum yields of fluorophores, respectively. We discovered specific molecu- lar substructures and fragments that determine the oscillator strengths of molecules from the feature importance analysis of our random forest machine. This discovery is expected to serve as a new design principle for novel fluorophores.