Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach

文化大學機構典藏 CCUR > 理工學院 > 工學院 > 化學工程與材料工程學系暨碩士班 > 期刊論文 > Item 987654321/48776

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题名:	Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach
作者:	Ye, ZR (Ye, Zong-Rong) Huang, IS (Huang, I-Shou) Chan, YT (Chan, Yu-Te) Li, ZJ (Li, Zhong-Ji) Liao, CC (Liao, Chen-Cheng) Tsai, HR (Tsai, Hao-Rong) Hsieh, MC (Hsieh, Meng-Chi) Chang, CC (Chang, Chun-Chih) Tsai, MK (Tsai, Ming-Kang)
贡献者:	化材系
关键词:	MODEL
日期:	2020-06-23
上传时间:	2020-11-03 15:31:42 (UTC+8)
摘要:	Organic fluorescent molecules play critical roles in fluorescence inspection, biological probes, and labeling indicators. More than ten thousand organic fluorescent molecules were imported in this study, followed by a machine learning based approach for extracting the intrinsic structural characteristics that were found to correlate with the fluorescence emission. A systematic informatics procedure was introduced, starting from descriptor cleaning, descriptor space reduction, and statistical-meaningful regression to build a broad and valid model for estimating the fluorescence emission wavelength. The least absolute shrinkage and selection operator (Lasso) regression coupling with the random forest model was finally reported as the numerical predictor as well as being fulfilled with the statistical criteria. Such an informatics model appeared to bring comparable predictive ability, being complementary to the conventional time-dependent density functional theory method in emission wavelength prediction, however, with a fractional computational expense.
關聯:	RSC ADVANCES 卷冊: 10 期: 40 頁數: 23834-23841
显示于类别:	[化學工程與材料工程學系暨碩士班] 期刊論文

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