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

Journal: Interdisciplinary sciences, computational life sciences
PMID: 31486019

Abstract

BACKGROUND: Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces.

Authors

  • Youquan Liu
    Research Institute of Natural Gas Technology, Petro China Southwest Oil and Gas Field Company, Chengdu, 610213, China. youquan_l@petrochina.com.cn.
  • Yanzhi Guo
    College of Chemistry, Sichuan University, Chengdu 610064, PR China. Electronic address: yzguo@scu.edu.cn.
  • Wengang Wu
    Research Institute of Natural Gas Technology, Petro China Southwest Oil and Gas Field Company, Chengdu, 610213, China.
  • Ying Xiong
    Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.
  • Chuan Sun
    Research Institute of Natural Gas Technology, Petro China Southwest Oil and Gas Field Company, Chengdu, 610213, China.
  • Li Yuan
    Research Institute of Natural Gas Technology, Petro China Southwest Oil and Gas Field Company, Chengdu, 610213, China.
  • Menglong Li
    College of Chemistry, Sichuan University, Chengdu 610064, PR China. Electronic address: liml@scu.edu.cn.

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