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.

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