Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning-Reference-Cited by-同舟云学术

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Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

Published:2022-09-30 Issue:2 Volume:101 Page:380-394
ISSN:1747-0277
Container-title:Chemical Biology & Drug Design
language:en
Short-container-title:Chem Biol Drug Des

Author:

Sun Linkang¹,Zhang Min²,Xie Liangxu¹,Xu Xiaojun¹,Xu Peng³,Xu Lei¹^ORCID

Affiliation:

1. Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering Jiangsu University of Technology Changzhou China

2. School of Computer Engineering Jiangsu University of Technology Changzhou China

3. Department of Orthopedics Second Military Medical University Affiliated Changzheng Hospital Shanghai China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Molecular Medicine,Biochemistry,Drug Discovery,Pharmacology,Organic Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cbdd.14137

Reference47 articles.

1. Partial least squares regression and projection on latent structure regression (PLS Regression)

2. Factors affecting the retention of polycyclic aromatic hydrocarbons in gas chromatography

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1. Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants;Molecules;2023-11-07

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