Photoswitches, Azobenzene, Quantitative structure–property relationship, Machine learning, Molecular descriptors, Information technology, T58.5-58.64, Chemistry, QD1-999

Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. The fragment counts have demonstrated the best performance in the benchmark for both properties. We validate the models in cross-validation and on an external set. The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. We also provide an interpretation of the modeling results using ColorAtom approach and the insights into the chemical space covered by the data set. Scientific contribution The paper provides a machine learning approach based only on structural features to predict two important photoswitch properties. Unlike previous studies, we do not use any quantum chemical features which accelerates the modeling procedure, while the accuracy of models remains high. Moreover, the fragment counts offer unique approach to model interpretation that is useful for rational design of photoswitches with desired properties.