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Prediction of Molecular Electronic Transitions Using Random Forests
Cited 16 time in
Web of Science
Cited 31 time in Scopus
- Authors
- Issue Date
- 2020-12
- Publisher
- American Chemical Society
- Citation
- Journal of Chemical Information and Modeling, Vol.60 No.12, pp.5984-5994
- 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.
- ISSN
- 1549-9596
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Related Researcher
- Graduate School of Convergence Science & Technology
- Dept. of Molecular and Biopharmaceutical Sciences
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