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Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging
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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039567/ https://www.ncbi.nlm.nih.gov/pubmed/36964565 http://dx.doi.org/10.1186/s12951-023-01864-9 |
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author | 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 |
author_facet | 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 |
author_sort | Zhang, Yibin |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-023-01864-9. |
format | Online Article Text |
id | pubmed-10039567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100395672023-03-26 Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging 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 J Nanobiotechnology Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-023-01864-9. BioMed Central 2023-03-25 /pmc/articles/PMC10039567/ /pubmed/36964565 http://dx.doi.org/10.1186/s12951-023-01864-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research 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 Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title | Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title_full | Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title_fullStr | Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title_full_unstemmed | Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title_short | Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
title_sort | machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039567/ https://www.ncbi.nlm.nih.gov/pubmed/36964565 http://dx.doi.org/10.1186/s12951-023-01864-9 |
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