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Rusanov, A.I.* ; Dmitrieva, O.A.* ; Mamardashvili, N.Z.* ; Tetko, I.V.

More is not always better: Local models provide accurate predictions of spectral properties of porphyrins.

Int. J. Mol. Sci. 23:1201 (2022)
Verlagsversion Forschungsdaten Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.26 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Absorbance Maximum Wavelength ; Chromophores ; Local Model ; Molar Extinction Coefficient ; Porphyrins ; Qspr ; Random Forest; Absorption Wavelength; Qspr; Domain; State
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1661-6596
e-ISSN 1422-0067
Quellenangaben Band: 23, Heft: 3, Seiten: , Artikelnummer: 1201 Supplement: ,
Verlag MDPI
Verlagsort Basel
Begutachtungsstatus Peer reviewed
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503000-001
Scopus ID 85122993766
PubMed ID 35163123
Erfassungsdatum 2022-06-07