Accurate prediction of B NMR chemical shift of BODIPYs 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 11 B 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 11 B 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 11 B 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 11 B NMR chemical shift of boron-containing dyes. We believe that the new model will make it easier for researchers to correctly interpret the 11 B NMR chemical shifts experimentally determined and to select more optimal conditions to perform an NMR experiment. We present the results of developing a new model based on machine learning methods for predicting the 11 B NMR chemical shift of boron-containing dyes.