Kang Beomchang, Seok Chaok, Lee Juyong
Department of Chemistry, Seoul National University, 08826 Seoul, Republic of Korea.
Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 24341 Chuncheon, Republic of Korea.
ACS Omega. 2021 Oct 5;6(41):27454-27465. doi: 10.1021/acsomega.1c04347. eCollection 2021 Oct 19.
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.
发现新型且优良的荧光团对于理解许多化学和生物学研究至关重要。高分辨率生物成像需要具有多种颜色和高量子产率的荧光团。分子的最大振子强度及其相应的吸收波长分别与荧光团的量子产率和发射光谱密切相关。因此，设计优良荧光团分子的核心步骤是优化分子所需的电子跃迁性质。在此，我们提出了MOLGENGO，一种新的分子性质优化算法，通过机器学习和全局优化来发现新型且优良的荧光团。本研究报告了来自MOLGENGO的具有高振子强度且吸收波长接近200、400和600 nm的新型分子。MOLGENGO模拟结果有可能成为新荧光团框架的候选者。
