Publications

Detailed Information

Prediction of Molecular Electronic Transitions Using Random Forests

Cited 16 time in Web of Science Cited 31 time in Scopus
Authors

Kang, Beomchang; Seok, Chaok; Lee, Ju Yong

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
URI
https://hdl.handle.net/10371/201516
DOI
https://doi.org/10.1021/acs.jcim.0c00698
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share