Jin Yongshi, Wang Zhaohe, Dong Miao, Sun Pingping, Chi Weijie
School of Cyberspace Security, Hainan University, Haikou 570228, China; School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China.
School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125213. doi: 10.1016/j.saa.2024.125213. Epub 2024 Sep 24.
Single benzene fluorophores (SBFs) have garnered significant research attention due to their ease of preparation, seamless diffusion into biological samples, and low molecular weight. Accurately predicting the molecular photophysical properties, specifically the maximum absorption and emission wavelengths, is pivotal in advancing functional SBFs. In this study, we introduce a machine-learning model to estimate the maximum absorption and emission wavelengths of SBFs precisely. This model leverages a Full Connect Neural Network and computational chemistry and is tailored to address the challenges associated with a relatively small dataset (81 SBFs). Remarkably, our model (SBFs-ML) demonstrates impressive accuracy, yielding a mean relative error of 1.54 % and 2.93 % for SBFs' maximum absorption and emission wavelengths, respectively. Importantly, the SBFs-ML was bullied based on only three descriptors, resulting in strong interpretability. Experimental results have strongly corroborated these predictions. Our prediction methods are poised to facilitate significantly the efficient design and creation of SBFs.
单苯荧光团（SBFs）因其易于制备、能无缝扩散到生物样品中且分子量低而受到了大量的研究关注。准确预测分子光物理性质，特别是最大吸收波长和发射波长，对于推进功能性SBFs至关重要。在本研究中，我们引入了一种机器学习模型来精确估计SBFs的最大吸收波长和发射波长。该模型利用全连接神经网络和计算化学，专门用于应对与相对较小的数据集（81个SBFs）相关的挑战。值得注意的是，我们的模型（SBFs-ML）表现出了令人印象深刻的准确性，对于SBFs的最大吸收波长和发射波长，平均相对误差分别为1.54%和2.93%。重要的是，SBFs-ML仅基于三个描述符构建，具有很强的可解释性。实验结果有力地证实了这些预测。我们的预测方法有望极大地促进SBFs的高效设计和创制。
