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 computational 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. Excitation energies and oscillator strengths are directly related to the emission spectrum and the quantum yields of fluorophores, respectively. We discovered specific molecular 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.

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

The discovery of novel and favorable fluorophores is critical for understanding many chemical and biological studies. High-resolution biological imaging necessitates fluorophores with diverse colors and high quantum yields. The maximum oscillator strength and its corresponding absorption wavelength of a molecule are closely related to the quantum yields and the emission spectrum of fluorophores, respectively. Thus, the core step to design favorable fluorophore molecules is to optimize the desired electronic transition properties of molecules. Here, we present MOLGENGO, a new molecular property optimization algorithm, to discover novel and favorable fluorophores with machine learning and global optimization. This study reports novel molecules from MOLGENGO with high oscillator strength and absorption wavelength close to 200, 400, and 600 nm. The results of MOLGENGO simulations have the potential to be candidates for new fluorophore frameworks.

Fluorophores play crucial roles in chemical and biological imaging. An efficient computational model that evaluates the electronic properties of molecules accurately would be a useful tool for discovering novel fluorophores. Tree‐based ensemble and graph neural network (GNN) methods have been regarded as attractive models for predicting molecular properties. Here, we present a benchmark test using three tree‐based ensemble methods (Random Forest, LightGBM, and XGBoost) and three GNNs (directed message passing neural network [D‐MPNN], attention message passing neural network [AMPNN], and DimeNet++) for predicting electronic transition properties such as excitation energies and oscillator strengths. From our benchmark, DimeNet++ was identified as the most accurate model to predict electronic transition properties. The average root mean square error of DimeNet++ for predicting the HOMO–LUMO gap was 0.11 eV whereas those of the other methods exceeded 0.3 eV. D‐MPNN predicted fastest without sacrificing accuracy. Our results show that DimeNet++ and D‐MPNN may serve as helpful evaluators for novel fluorophore design when combined with molecular generation methods.

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