ArticleJournal of fluorescence2018
Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions.
Article in Journal of fluorescence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.Journal of chemical information and modeling · 2024Article
- Insights into Molecular Structure of Pterins Suitable for Biomedical Applications.International journal of molecular sciences · 2022Review
- Multi-fidelity prediction of molecular optical peaks with deep learning.Chemical science · 2022Article
- Identification of novel neuroblastoma biomarkers in urine samples.Scientific reports · 2021Article
- Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations.Journal of computer-aided molecular design · 2021Article
- Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications.Journal of cheminformatics · 2020Article
- Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 [Formula: see text] of 0.940 and 0.905 for λ
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Registered trials
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