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.
In this article, we present the results of developing a model based on RFR machine learning method using the ISIDA fragment descriptors for predicting the 11B NMR chemical shift of...
This review presents an analysis of different algorithms for predicting the sensory ability of organic compounds towards metal ions based on their chemical formula. A database of chemosensors containing information on various classes of suitable compounds, including dipyrromethenes, BODIPY, Schiff bases, hydrazones, fluorescein, rhodamine, phenanthroline, coumarin, naphthalimide derivatives, and others (a total of 965 molecules) has been compiled. Additionally, a freely available software has been developed for predicting the sensing ability of chemical compounds, which can be accessed through a Telegram bot. This tool aims to assist researchers in their search for new chemosensors.
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