Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach
| Autor: | Ming Kang Tsai, Chun Chih Chang, Chen Cheng Liao, Yu Te Chan, Hao Rong Tsai, Meng Chi Hsieh, I. Shou Huang, Zong Rong Ye, Zhong Ji Li |
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| Rok vydání: | 2020 |
| Předmět: |
Quantitative structure–activity relationship
business.industry Computer science General Chemical Engineering General Chemistry Machine learning computer.software_genre Fluorescence Regression Random forest Reduction (complexity) Wavelength Lasso (statistics) Density functional theory Artificial intelligence business computer |
| Zdroj: | RSC advances. 10(40) |
| ISSN: | 2046-2069 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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