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

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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 \( {R}_{pred}^2 \) 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.

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References

  1. Carter KP, Young AM, Palmer AE (2014) Fluorescent Sensors for Measuring Metal Ions in Living Systems. Chem Rev 114:4564–4601. https://doi.org/10.1021/cr400546e

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Yue Y, Huo F, Yin C et al (2015) A new “donor-two-acceptor” red emission fluorescent probe for highly selective and sensitive detection of cyanide in living cells. Sensors Actuators B Chem 212:451–456. https://doi.org/10.1016/j.snb.2015.02.074

    Article  CAS  Google Scholar 

  3. Guo Z, Park S, Yoon J, Shin I (2014) Recent progress in the development of near-infrared fluorescent probes for bioimaging applications. Chem Soc Rev 43:16–29. https://doi.org/10.1039/C3CS60271K

    Article  PubMed  Google Scholar 

  4. Basabe-Desmonts L, Reinhoudt DN, Crego-Calama M (2007) Design of fluorescent materials for chemical sensing. Chem Soc Rev 36:993–1017. https://doi.org/10.1039/B609548H

    Article  PubMed  CAS  Google Scholar 

  5. Guillaumont D, Nakamura S (2000) Calculation of the absorption wavelength of dyes using time-dependent density-functional theory (TD-DFT). Dyes Pigments 46:85–92. https://doi.org/10.1016/S0143-7208(00)00030-9

    Article  CAS  Google Scholar 

  6. Åstrand P-O, Ramanujam PS, Hvilsted S et al (2000) Ab Initio Calculation of the Electronic Spectrum of Azobenzene Dyes and Its Impact on the Design of Optical Data Storage Materials. J Am Chem Soc 122:3482–3487. https://doi.org/10.1021/ja993154r

    Article  CAS  Google Scholar 

  7. De la Fuente JR, Cañete A, Saitz C, Jullian C (2002) Photoreduction of 3-Phenylquinoxalin-2-ones by Amines: Transient-Absorption and Semiempirical Quantum-Chemical Studies. J Phys Chem A 106:7113–7120. https://doi.org/10.1021/jp014317c

    Article  CAS  Google Scholar 

  8. Jacquemin D, Perpète EA, Scuseria GE et al (2008) TD-DFT Performance for the Visible Absorption Spectra of Organic Dyes: Conventional versus Long-Range Hybrids. J Chem Theory Comput 4:123–135. https://doi.org/10.1021/ct700187z

    Article  PubMed  CAS  Google Scholar 

  9. Zhao Y, Zhao J, Huang Y et al (2014) Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method. J Hazard Mater 278:320–329. https://doi.org/10.1016/j.jhazmat.2014.06.018

    Article  PubMed  CAS  Google Scholar 

  10. Venkatraman V, Alsberg BK (2015) A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes. Dyes Pigments 114:69–77. https://doi.org/10.1016/j.dyepig.2014.10.026

    Article  CAS  Google Scholar 

  11. Kar S, Sizochenko N, Ahmed L et al (2016) Quantitative structure-property relationship model leading to virtual screening of fullerene derivatives: Exploring structural attributes critical for photoconversion efficiency of polymer solar cell acceptors. Nano Energy 26:677–691. https://doi.org/10.1016/j.nanoen.2016.06.011

    Article  CAS  Google Scholar 

  12. Pereira F, Xiao K, Latino DARS et al (2017) Machine learning methods to predict density functional theory B3LYP energies of HOMO and LUMO Orbitals. J Chem Inf Model 57:11–21. https://doi.org/10.1021/acs.jcim.6b00340

    Article  PubMed  CAS  Google Scholar 

  13. Xu J, Zheng Z, Chen B, Zhang Q (2006) A linear QSPR model for prediction of maximum absorption wavelength of second-order NLO chromophores. QSAR Comb Sci 25:372–379. https://doi.org/10.1002/qsar.200530143

    Article  CAS  Google Scholar 

  14. Nantasenamat C, Isarankura-Na-Ayudhya C, Tansila N et al (2007) Prediction of GFP spectral properties using artificial neural network. J Comput Chem 28:1275–1289. https://doi.org/10.1002/jcc.20656

    Article  PubMed  CAS  Google Scholar 

  15. Shi J, Luan F, Zhang H et al (2006) QSPR study of fluorescence wavelengths (λex/λem) based on the heuristic method and radial basis function neural networks. QSAR Comb Sci 25:147–155. https://doi.org/10.1002/qsar.200510142

    Article  CAS  Google Scholar 

  16. Li M, Ni N, Wang B, Zhang Y (2008) Modeling the excitation wavelengths (λex) of boronic acids. J Mol Model 14:441–449. https://doi.org/10.1007/s00894-008-0293-0

    Article  PubMed  CAS  Google Scholar 

  17. Xu J, Xiong Q, Chen B et al (2008) Modeling the relative fluorescence intensity ratio of Eu(III) complex in different solvents based on QSPR method. J Fluoresc 19:203. https://doi.org/10.1007/s10895-008-0403-5

    Article  PubMed  CAS  Google Scholar 

  18. Beheshti A, Riahi S, Ganjali MR, Norouzi P (2012) Highlighting and trying to overcome a serious drawback with qspr studies; data collection in different experimental conditions (mixed-QSPR). J Comput Chem 33:732–747. https://doi.org/10.1002/jcc.22892

    Article  PubMed  CAS  Google Scholar 

  19. Marini A, Muñoz-Losa A, Biancardi A, Mennucci B (2010) What is Solvatochromism? J Phys Chem B 114:17128–17135. https://doi.org/10.1021/jp1097487

    Article  PubMed  CAS  Google Scholar 

  20. Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  21. fluorophores.org. http://www.fluorophores.tugraz.at/. Accessed 1 May 2007

  22. Weber G, Farris FJ (1979) Synthesis and spectral properties of a hydrophobic fluorescent probe: 6-propionyl-2-(dimethylamino)naphthalene. Biochemistry 18:3075–3078. https://doi.org/10.1021/bi00581a025

    Article  PubMed  CAS  Google Scholar 

  23. Kucherak OA, Didier P, Mély Y, Klymchenko AS (2010) Fluorene Analogues of Prodan with Superior Fluorescence Brightness and Solvatochromism. J Phys Chem Lett 1:616–620. https://doi.org/10.1021/jz9003685

    Article  CAS  Google Scholar 

  24. Lu Z, Lord SJ, Wang H et al (2006) Long-wavelength analogue of PRODAN: synthesis and properties of Anthradan, a fluorophore with a 2,6-Donor−Acceptor Anthracene Structure. J Org Chem 71:9651–9657. https://doi.org/10.1021/jo0616660

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. ChemAxon (2017) Marvin 17.28.0

  26. Berthold MR, Cebron N, Dill F et al (2009) KNIME - the Konstanz Information Miner: Version 2.0 and Beyond. SIGKDD Explor Newsl 11:26–31. https://doi.org/10.1145/1656274.1656280

    Article  Google Scholar 

  27. Karelson M (2000) Molecular descriptors in QSAR/QSPR

  28. Kode - Chemoinformatics (2016) Dragon version 7.0.4

  29. Frisch MJ, Trucks GW, Schlegel HB, et al (2016) Gaussian 09 Revision A.02

  30. Becke AD (1993) A new mixing of Hartree–Fock and local density-functional theories. J Chem Phys 98:1372–1377. https://doi.org/10.1063/1.464304

    Article  CAS  Google Scholar 

  31. Batista GE, Monard MC, others (2002) A Study of K-Nearest Neighbour as an Imputation Method. HIS 87:48

  32. Breiman L (1984) Classification and regression trees. Routledge, New York

    Google Scholar 

  33. Lorber A, Wangen LE, Kowalski BR (1987) A theoretical foundation for the PLS algorithm. J Chemom 1:19–31. https://doi.org/10.1002/cem.1180010105

    Article  CAS  Google Scholar 

  34. Drucker H, Burges CJC, Kaufman L, et al (1997) Support vector regression machines. In: Advances in neural information processing systems. pp 155–161

  35. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Rev 11:203–224

    Google Scholar 

  36. Sharnoff M (1971) Photophysics of aromatic molecules. J Lumin. https://doi.org/10.1016/0022-2313(71)90011-1

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Authors and Affiliations

  1. Department of Chemical Systems Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Chia-Hsiu Chen, Kenichi Tanaka & Kimito Funatsu

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  1. Chia-Hsiu Chen
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  2. Kenichi Tanaka
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  3. Kimito Funatsu
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Correspondence to Kimito Funatsu.

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Chen, CH., Tanaka, K. & Funatsu, K. Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions. J Fluoresc 28, 695–706 (2018). https://doi.org/10.1007/s10895-018-2233-4

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  • DOI: https://doi.org/10.1007/s10895-018-2233-4

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