Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical... The Quantitative Structure – Property Relationship (QSPR) approach was performed to study the fluorescence absorption wavelengths and emission wavelengths of 413 fluorescent dyes in different solvent conditions. The dyes included the chromophore derivatives of cyanine, xanthene, coumarin, pyrene, naphthalene, anthracene and etc., with the wavelength ranging from 250 nm to 800 nm. An ensemble method, random forest (RF), was employed to construct nonlinear prediction models compared with the results of linear partial least squares and nonlinear support vector machine regression models. Quantum chemical descriptors derived from density functional theory method and solvent information were also used by constructing models. The best prediction results were obtained from RF model, with the squared correlation coefficients R pred 2 $$ {R}_{pred}^2 $$ of 0.940 and 0.905 for λabs and λem, respectively. The descriptors used in the models were discussed in detail in this report by comparing the feature importance of RF. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Fluorescence Springer Journals

Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Biomedicine; Biomedicine, general; Biological and Medical Physics, Biophysics; Biotechnology; Biochemistry, general; Analytical Chemistry
ISSN
1053-0509
eISSN
1573-4994
D.O.I.
10.1007/s10895-018-2233-4
Publisher site
See Article on Publisher Site

Abstract

The Quantitative Structure – Property Relationship (QSPR) approach was performed to study the fluorescence absorption wavelengths and emission wavelengths of 413 fluorescent dyes in different solvent conditions. The dyes included the chromophore derivatives of cyanine, xanthene, coumarin, pyrene, naphthalene, anthracene and etc., with the wavelength ranging from 250 nm to 800 nm. An ensemble method, random forest (RF), was employed to construct nonlinear prediction models compared with the results of linear partial least squares and nonlinear support vector machine regression models. Quantum chemical descriptors derived from density functional theory method and solvent information were also used by constructing models. The best prediction results were obtained from RF model, with the squared correlation coefficients R pred 2 $$ {R}_{pred}^2 $$ of 0.940 and 0.905 for λabs and λem, respectively. The descriptors used in the models were discussed in detail in this report by comparing the feature importance of RF.

Journal

Journal of FluorescenceSpringer Journals

Published: Apr 22, 2018

References

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