In silico prediction of boiling point, octanol–water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning-Reference-Cited by-同舟云学术

In silico prediction of boiling point, octanol–water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning

Published:2022-07-28 Issue:1 Volume:101 Page:52-68
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,Gao Qian²,Xu Xiaojun¹,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

Funder

Natural Science Foundation of Jiangsu Province

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.14121

Reference42 articles.

1. Overview of Polycyclic Aromatic Compounds (PAC)

2. Quantitative Structure−Property Relationships (QSPRs) for the Estimation of Vapor Pressure: A Hierarchical Approach Using Mathematical Structural Descriptors

3. Molecular fingerprint similarity search in virtual screening

4. ADME Evaluation in Drug Discovery. 10. Predictions of P-Glycoprotein Inhibitors Using Recursive Partitioning and Naive Bayesian Classification Techniques

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