Data-driven machine learning models for predicting the maximum absorption and emission wavelengths of single benzene fluorophores.

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.