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

Zong Rong Ye, I. Shou Huang, Yu Te Chan, Zhong Ji Li, Chen Cheng Liao, Hao Rong Tsai, Meng Chi Hsieh, Chun Chih Chang, Ming Kang Tsai

Research output: Contribution to journalArticlepeer-review

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. This journal is

Original languageEnglish
Pages (from-to)23834-23841
Number of pages8
JournalRSC Advances
Volume10
Issue number40
DOIs
Publication statusPublished - 2020 Jun 23

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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