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Ksenofontov, A.A.* ; Lukanov, M.M.* ; Bocharov, P.S.* ; Berezin, M.B.* ; Tetko, I.V.

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

Spectrochim. Acta A 267:120577 (2022)
Postprint DOI PMC
Open Access Green
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Absorption Maximum Wavelength ; Bodipy ; Deep Neural Networks ; Ochem ; Qspr
ISSN (print) / ISBN 1386-1425
e-ISSN 1873-3557
Journal Spectrochimica acta, Part A
Quellenangaben Volume: 267, Issue: 2, Pages: , Article Number: 120577 Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Structural Biology (STB)