Abstract
Fluorescent probes are pivotal in biosensing and bioimaging, necessitating precise spectral tailoring for high-performance applications. Despite their importance, probe design remains largely empirical, a process that is both time-consuming and laborious. To streamline this, we created a comprehensive dataset of over 600 rhodamine fluorescent probes and employed a multilayer interaction convolutional model (MICNet) trained on molecular fingerprints to accurately predict excitation and emission wavelengths. Our model demonstrated high accuracy with mean relative errors (MRE) of 0.1% for excitation and 0.4% for emission wavelengths. Advancing this, we implemented a closed-loop strategy that integrates experimental feedback to iteratively enhance the design algorithm's accuracy, thereby improving the probes' performance and reliability. This method not only accelerates the probe development cycle but also facilitates the creation of spectrally customized fluorescent probes, offering a significant advancement in the field of bioanalytical chemistry.