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
• DOI: 10.1186/s12951-023-01864-9
• ISSN: 1477-3155

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 using conventional chemical synthesis route. Fortunately, artificial intelligence techniques that could predict the properties of AIEgen molecules would be helpful and valuable for novel AIEgens design and synthesis. In this work, we applied machine learning (ML) techniques to screen AIEgens with expected excitation and emission wavelength for biomedical deep fluorescence imaging. First, a database of various AIEgens collected from the literature was established. Then, by extracting key features using molecular descriptors and training various state-of-the-art ML models, a multi-modal molecular descriptors strategy has been proposed to extract the structure-property relationships of AIEgens and predict molecular absorption and emission wavelength peaks. Compared to the first principles calculations, the proposed strategy provided greater accuracy at a lower computational cost. Finally, three newly predicted AIEgens with desired absorption and emission wavelength peaks were synthesized successfully and applied for cellular fluorescence imaging and deep penetration imaging. All the results were consistent successfully with our expectations, which demonstrated the above ML has a great potential for screening AIEgens with suitable wavelengths, which could boost the design and development of novel organic fluorescent materials.