Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging

• Published in: Journal of nanobiotechnology Vol. 21; no. 1; pp. 107 - 17
• Main Authors: Zhang, Yibin, Fan, Miaozhuang, Xu, Zhourui, Jiang, Yihang, Ding, Huijun, Li, Zhengzheng, Shu, Kaixin, Zhao, Mingyan, Feng, Gang, Yong, Ken-Tye, Dong, Biqin, Zhu, Wei, Xu, Gaixia
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.03.2023; BioMed Central; BMC
• Subjects: Absorption; Accuracy; Agglomeration; Aggregation-induced emission; Algorithms; Artificial Intelligence; Biocompatibility; Biomedical materials; Chemical synthesis; Computer applications; Datasets; Emission; Emissions (Pollution); First principles; Fluorescence; Fluorescence imaging; Fluorescent Dyes - chemistry; Imaging; Imaging systems; Learning algorithms; Lumiongens; Machine Learning; Materials research; Methods; Molecular absorption; Molecular modelling; Molecular structure; Molecular weight; Neural networks; Optical Imaging - methods; Optical properties; Performance evaluation; Screening; Solvents; Support vector machines; Wavelength; Wavelengths; China; Fluorescence imaging; Aggregation-induced emission; Lumiongens; Artificial intelligence; Machine learning

Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special applications takes a lot of time and efforts by us...