Effect of meso-substituents and solvent on the photo- and thermal stability of BODIPY dyes

Effect of meso-substituents and medium properties on the photo- and pH-stability, penetration efficiency into bacterial and microscopic fungi cells of terpene-BODIPY conjugates

In order to expand the arsenal of tools and areas for practical use of BODIPY dyes as bifunctional fluorescent theranostics, we studied the effect of the meso-substituents nature and medium properties on photo- and pH-stability, efficiency of singlet oxygen generation, and affinity to biostructures of terpene-BODIPY conjugates. The BODIPYs fused with myrtenol or thiotherpenoid via carboxylic acid residues exhibit high stability over a wide pH range and the presence of a bulky substituent at the meso-position of BODIPY conjugates increases their photostability two-fold compared to structurally related meso-unsubstituted analogues. Furthermore, the photodegradation rate of the conjugates directly depends on their ability to generate singlet oxygen and the course probability of the corresponding red-ox reactions involving reactive oxygen species. The conjugate of BODIPY with a thiotherpenoid demonstrated high ability to penetrate the membranes of filamentous and yeast-like fungi and bind to membrane of organelles in the fungal cell. At the same time, this compound also had a high ability to penetrate into biofilms of Staphylococcus aureus and Klebsiella pneumoniae and into bacterial cells within the matrix, which makes this compound promising for staining intracellular structures of eukaryotic cells and bacteria embedded into biofilms.

Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning

In this article, we present the results of developing a model based on an RFR machine learning method using the ISIDA fragment descriptors for predicting the ¹¹B NMR chemical shift of BODIPYs. The model is freely available at https://ochem.eu/article/146458. The model demonstrates the high quality of predicting the ¹¹B NMR chemical shift (RMSE, 5CV (FINALE training set) = 0.40 ppm, RMSE (TEST set) = 0.14 ppm). In addition, we compared the "cost" and the user-friendliness for calculations using the quantum-chemical model with the DFT/GIAO approach. The ¹¹B NMR chemical shift prediction accuracy (RMSE) of the model considered is more than three times higher and tremendously faster than the DFT/GIAO calculations. As a result, we provide a convenient tool and database that we collected for all researchers, that allows them to predict the ¹¹B NMR chemical shift of boron-containing dyes. We believe that the new model will make it easier for researchers to correctly interpret the ¹¹B NMR chemical shifts experimentally determined and to select more optimal conditions to perform an NMR experiment.