A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection-Reference-Cited by-同舟云学术

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A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection

Published:2019-09-04 Issue:4 Volume:11 Page:738-747
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Liu Youquan,Guo Yanzhi,Wu Wengang,Xiong Ying,Sun Chuan,Yuan Li,Li Menglong

Funder

Major Science and Technology Project of China National Petroleum Co. Ltd

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

http://link.springer.com/content/pdf/10.1007/s12539-019-00346-7.pdf

Reference50 articles.

1. Mikhailovskii AI, Petrov NA (1997) Monitoring of underground pipeline corrosion condition with sensory instruments. Prot Met 33:293–295

2. Panchenko YM, Marshakov AI, Igonin TN, Kovtanyuk VV, Nikolaeva LA (2014) Long-term forecast of corrosion mass losses of technically important metals in various world regions using a power function. Corros Sci 88:306–316

3. Yıldız R (2015) An electrochemical and theoretical evaluation of 4,6-diamino-2-pyrimi-dinethiol as a corrosion inhibitor for mild steel in HCl solutions. Corros Sci 90:544–553

4. Spahr S, Huntscha S, Bolotin MP, Maier J, Elsner M, Hollender J (2013) Compound-specific isotope analysis of benzotriazole and its derivatives. Anal Bioanal Chem 405:2843–2856

5. Abd EAEE, Abd EWS, Farouk A, Abd EHSM (2013) Factors affecting the corrosion behaviour of aluminium in acid solutions. II. Inorganic additives as corrosion inhibitors for Al in HCl solutions. Corros Sci 68:14–24

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