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.

Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?

In this article, we provide a convenient tool for all researchers to predict the value of the molar absorption coefficient for a wide number of dyes without any computer costs. The new model is based on RFR method (ALogPS, OEstate + Fragmentor + QNPR) and is able to predict the molar absorption coefficient with an accuracy (5-fold cross-validation RMSE) of 0.26 log unit. This accuracy was achieved due to the fact that the model was trained on data for more than 20,000 unique dye molecules. To our knowledge, this is the first model for predicting the molar absorption coefficient trained on such a large and diverse set of dyes. The model is available at https://ochem.eu/article/145413. We hope that the new model will allow researchers to predict dyes with practically significant spectral characteristics and verify existing experimental data.

Aggregation behavior and spectroscopic properties of red-emitting distyryl-BODIPY in aqueous solution, Langmuir-Schaefer films and Pluoronic® F127 micelles.

Red-emitting distyryl substituted BODIPY dyes are among the most promising luminophors for bioimaging and optics applications. However, the practical application of BODIPYs is limited due to their high hydrophobicity and tendency to aggregate in aqueous organic solutions and solid phase. In this article, we propose an elegant solution to this problem. To this end, we carried out the detailed experimental and quantum-chemical study of the structural and spectral features of BF2-ms-phenyl-5,5'-bis(4-dimethylaminostyryl)-3,3'-dimethyl-2,2'-dipyrromethene (distyryl-BDP). The particular attention was paid to analysis of high sensitivity of the distyryl-BDP spectral characteristics to the solvent properties, and also the aggregation behavior features both in water-organic media and in mono- and multilayer Langmuir-Schaefer films. We selected the best conditions to obtain the hydrophilic micellar structures of distyryl-BDP with Pluronic® F127 having a high efficiency of dye solubilization. This method increasing the solubility improves the distyryl-BDP transport efficiency in physiological aqueous media. The aqueous solutions of distyryl-BDP-Pl micelles show the intense fluorescence in the phototherapy window region (λfl = 739 nm).

Deep neural network model for highly accurate prediction of BODIPYs absorption.

A possibility to accurately predict the absorption maximum wavelength of BODIPYs was investigated. We found that previously reported models had a low accuracy (40-57 nm) to predict BODIPYs due to the limited dataset sizes and/or number of BODIPYs (few hundreds). New models developed in this study were based on data of 6000-plus fluorescent dyes (including 4000-plus BODIPYs) and the deep neural network architecture. The high prediction accuracy (five-fold cross-validation room mean squared error (RMSE) of 18.4 nm) was obtained using a consensus model, which was more accurate than individual models. This model provided the excellent accuracy (RMSE of 8 nm) for molecules previously synthesized in our laboratory as well as for prospective validation of three new BODIPYs. We found that solvent properties did not significantly influence the model accuracy since only few BODIPYs exhibited solvatochromism. The analysis of large prediction errors suggested that compounds able to have intermolecular interactions with solvent or salts were likely to be incorrectly predicted. The consensus model is freely available at https://ochem.eu/article/134921 and can help the other researchers to accelerate design of new dyes with desired properties.