Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518055, China.
School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
J Nanobiotechnology. 2023 Mar 25;21(1):107. doi: 10.1186/s12951-023-01864-9.
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.
由于具有出色的生物相容性物理化学性能，具有聚集诱导发射（AIEgen）特性的发光体在最近的生物医学荧光成像中发挥了重要作用。然而，通过传统的化学合成途径筛选特殊应用的 AIEgen 需要大量的时间和精力。幸运的是，能够预测 AIEgen 分子性质的人工智能技术对于新型 AIEgen 的设计和合成将是有帮助和有价值的。在这项工作中，我们应用机器学习（ML）技术筛选用于生物医学深度荧光成像的具有预期激发和发射波长的 AIEgen。首先，从文献中收集了各种 AIEgen 的数据库。然后，通过使用分子描述符提取关键特征并训练各种最先进的 ML 模型，提出了一种多模态分子描述符策略，以提取 AIEgen 的结构-性质关系并预测分子吸收和发射波长峰。与第一性原理计算相比，该策略以较低的计算成本提供了更高的准确性。最后，成功合成了三个具有预期吸收和发射波长峰的新预测 AIEgen，并将其应用于细胞荧光成像和深度穿透成像。所有结果都与我们的预期一致，这表明上述 ML 技术在筛选具有合适波长的 AIEgen 方面具有很大的潜力，这将推动新型有机荧光材料的设计和开发。
