Harnessing machine learning for the rational design of high-performance fluorescent dyes

Abstract

The design of fluorescent dyes with optimized performance is crucial for advancements in various fields, including bioimaging, diagnostics, and optoelectronics. Traditional approaches to dye design often rely on trial-and-error experimentation, which can be time-consuming and resource-intensive. 42 ML models are tried for each property. One best model is selected for each property. Gradient boosting regressor is best model for the prediction of excitation values while extra trees regressor is best model for the prediction of emission values. A database of 5000 new dyes is generated and analyzed. 30 dyes with higher excitation and emission values are selected. Synthetic accessibility analysis is done for 30 dyes and majority of dyes are easy to synthesized. Our results demonstrate that ML-assisted design can significantly accelerate the discovery process, reduce the need for costly experimental iterations, and lead to the development of dyes with tailored properties for specific applications.