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

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/48776

Title:	Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach
Authors:	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)
Contributors:	化材系
Keywords:	MODEL
Date:	2020-06-23
Issue Date:	2020-11-03 15:31:42 (UTC+8)
Abstract:	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.
Relation:	RSC ADVANCES 卷冊: 10 期: 40 頁數: 23834-23841
Appears in Collections:	[Department of Chemical & Materials Engineering] journal articles

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