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结合量子化学描述符和溶剂条件的荧光性质定量构效关系研究的随机森林方法。

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

作者信息

Chen Chia-Hsiu, Tanaka Kenichi, Funatsu Kimito

机构信息

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

出版信息

J Fluoresc. 2018 Mar;28(2):695-706. doi: 10.1007/s10895-018-2233-4. Epub 2018 Apr 22.

DOI:10.1007/s10895-018-2233-4
PMID:29680928
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 and λ, respectively. The descriptors used in the models were discussed in detail in this report by comparing the feature importance of RF.

摘要

采用定量结构-性质关系(QSPR)方法研究了413种荧光染料在不同溶剂条件下的荧光吸收波长和发射波长。这些染料包括花青、呫吨、香豆素、芘、萘、蒽等发色团衍生物,波长范围为250纳米至800纳米。与线性偏最小二乘法和非线性支持向量机回归模型的结果相比,采用集成方法随机森林(RF)构建非线性预测模型。通过构建模型,还使用了从密度泛函理论方法和溶剂信息中导出的量子化学描述符。RF模型获得了最佳预测结果,λ和λ的平方相关系数[公式:见正文]分别为0.940和0.905。通过比较RF的特征重要性,本报告详细讨论了模型中使用的描述符。

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