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

机译:QSPR QSPR荧光性能研究组合量子化学描述函数及溶剂条件的QSPR研究

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摘要

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 of 0.940 and 0.905 for lambda(abs) and lambda(em), respectively. The descriptors used in the models were discussed in detail in this report by comparing the feature importance of RF.
机译:进行定量结构 - 性质关系(QSPR)方法,以研究不同溶剂条件下413荧光染料的荧光吸收波长和发射波长。染料包括氰基,黄嘌呤,香豆素,芘,萘,萘,萘等的发色团衍生物,波长范围为250nm至800nm。与线性部分最小二乘和非线性支持向量机回归模型相比,使用随机森林(RF),随机森林(RF)构成非线性预测模型。还通过构建模型来使用源自密度官能理论方法和溶剂信息的量子化学描述符。从RF模型获得最佳预测结果,分别具有0.940和0.905的平方相关系数,分别用于λ(ABS)和λ(EM)。通过比较RF的特征重要性,在本报告中详细讨论了模型中使用的描述符。

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