Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach-Reference-Cited by-同舟云学术

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

Published:2020 Issue:40 Volume:10 Page:23834-23841
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Ye Zong-Rong¹²³⁴,Huang I.-Shou¹²³⁴¹,Chan Yu-Te¹²³⁴⁵,Li Zhong-Ji¹²³⁴,Liao Chen-Cheng¹²³⁴,Tsai Hao-Rong¹²³⁴,Hsieh Meng-Chi¹²³⁴,Chang Chun-Chih¹²³⁴⁶,Tsai Ming-Kang¹²³⁴^ORCID

Affiliation:

1. Department of Chemistry

2. National Taiwan Normal University

3. Taipei

4. Taiwan

5. Theoretical Chemistry and Catalysis Research Center

6. Department of Chemical and Materials Engineering

Abstract

The combinatorial QSAR and machine learning approach provides the qualitative and computationally efficient prediction for fluorescence emission wavelength of organic molecules.

Funder

Ministry of Science and Technology, Taiwan

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/RA/D0RA05014H

Reference32 articles.

1. A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging

2. Journey of Aggregation-Induced Emission Research

3. E.Kim and S. B.Park , in Advanced Fluorescence Reporters in Chemistry and Biology , Springer , Berlin Heidelberg , 2010 , pp. 149–186