SpecML: web tool for predicting the spectral properties of BODIPYs

In this paper, we present the results of training machine learning (ML) models for accurate prediction of several key photophysical characteristics (absorption maximum wavelength, molar absorption coefficient, emission maximum wavelength, fluorescence quantum yield and lifetime, singlet oxygen generation quantum yield) for BODIPYs. ML models were trained using experimental data comprising more than 35,000 records for the predicted parameters. Particular emphasis was placed on model interpretability and on accounting for the solvent nature effect on the predicted photophysical parameters. To ensure open data access and a user-friendly interface, all developed models were integrated into the created web tool SpecML (http://specml.isc-ras.ru/). SpecML allows prediction of photophysical parameters for individual BODIPY molecules and the screening of entire series of BODIPYs. We believe that our created SpecML web tool will become an effective resource for accelerating the rational design of BODIPYs with desired photophysical properties and will be useful for a wide range of researchers in the fields of photonics, organic electronics, and molecular design.