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MOLGENGO: Finding Novel Molecules with Desired Electronic Properties by Capitalizing on Their Global Optimization

Cited 3 time in Web of Science Cited 3 time in Scopus

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

Issue Date
American Chemical Society
ACS Omega, Vol.6 No.41, pp.27454-27465
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.
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  • 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


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