Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review

Quadri Taiwo W.; Olasunkanmi Lukman O.; Fayemi Omolola E.; Akpan Ekemini D.; Verma Chandrabhan; Sherif El-Sayed M.; Khaled Khaled F.; Ebenso Eno E.

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Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review

机译：定量结构活动关系与人工神经网络作为预测金属表面有机化合物配位能力的重要工具：综述

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It has been well-established that organic corrosion inhibitors often form protective film through coordinate bonding with the metal. Different computational and experimental methods are used to describe the nature and effectiveness of such metal-inhibitor bonds. Quantitative structure activity relationship (QSAR) is one of the most recent and reliable computational methods used to describe metal-inhibitor coordination, leading to corrosion inhibition. The quest for the design of new, high-performance environmentally benign compounds that can effectively impede corrosion without excessive large-scale experimental trials has heightened research interest in molecular structure-corrosion inhibition relationship. Correlation between corrosion inhibition potentials and molecular descriptors of organic compounds is becoming increasingly advanced. This is also as new techniques such as machine learning, artificial neural network (ANN), support vector machine (SVM) and genetic function approximation (GFA) are becoming more famous with advancement in computer technology. This review article presents a summary of previous works on the use of QSAR and ANN as predictive tools for metal-organic compound coordination towards corrosion inhibition. (C) 2021 Elsevier B.V. All rights reserved.

机译：已经很好地确定了有机腐蚀抑制剂通常通过与金属的坐标键合来形成保护膜。使用不同的计算和实验方法来描述这种金属抑制剂键的性质和有效性。定量结构活动关系（QSAR）是用于描述金属抑制剂协调的最新和可靠的计算方法之一，导致腐蚀抑制。寻求设计新的高性能环境良性化合物，可以有效地妨碍腐蚀而没有过度大规模的实验试验，对分子结构腐蚀抑制关系的研究兴趣提高。有机化合物的腐蚀抑制电位与分子描述符之间的相关性越来越先进。这也是机器学习，人工神经网络（ANN），支持向量机（SVM）和遗传函数近似（GFA）的新技术也变得更加着名，随着计算机技术的进步。该审查条提出了以前的作品，用于使用QSAR和ANN作为用于腐蚀抑制的金属有机复合协调的预测工具。（c）2021 elestvier b.v.保留所有权利。

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来源
《Coordination chemistry reviews》 |2021年第11期|214101.1-214101.27|共27页
作者
Quadri Taiwo W.; Olasunkanmi Lukman O.; Fayemi Omolola E.; Akpan Ekemini D.; Verma Chandrabhan; Sherif El-Sayed M.; Khaled Khaled F.; Ebenso Eno E.;
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作者单位

North West Univ Fac Nat & Agr Sci Sch Phys & Chem Sci Dept Chem Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa|North West Univ Fac Nat & Agr Sci Mat Sci Innovat & Modelling MaSIM Res Focus Area Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa;

Obafemi Awolowo Univ Fac Sci Dept Chem Ife 220005 Nigeria;

North West Univ Fac Nat & Agr Sci Sch Phys & Chem Sci Dept Chem Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa|North West Univ Fac Nat & Agr Sci Mat Sci Innovat & Modelling MaSIM Res Focus Area Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa;

North West Univ Fac Nat & Agr Sci Sch Phys & Chem Sci Dept Chem Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa|North West Univ Fac Nat & Agr Sci Mat Sci Innovat & Modelling MaSIM Res Focus Area Mafikeng Campus Private Bag X2046 ZA-2735 Mmabatho South Africa;

King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Adv Mat Dhahran 31261 Saudi Arabia;

King Saud Univ Ctr Excellence Res Engn Mat CEREM POB 800 Riyadh 11421 Saudi Arabia;

Ain Shams Univ Fac Educ Chem Dept Electrochem Res Lab Cairo Egypt;

Univ South Africa Coll Sci Engn & Technol Inst Nanotechnol & Water Sustainabil ZA-1710 Johannesburg South Africa;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);
原文格式 PDF
正文语种 eng
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关键词
Corrosion; Artificial neural network; Quantitative structure activity relationship; Organic compounds;

机译：腐蚀;人工神经网络;定量结构活动关系;有机化合物;

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机译：使用关联神经网络预测有机化合物表面张力的定量构效关系
2. Quantitative Structure-Property Relationship for Predicting Surface Tension of Organic Compounds Using Associative Neural Networks [J] . P. Neelamegam, S. Krishnaraj Asian Journal of Chemistry: An International Quarterly Research Journal of Chemistry . 2013,第5期

机译：使用关联神经网络预测有机化合物表面张力的定量构效关系
3. Quantitative structure-property relationships of electroluminescent materials: Artificial neural networks and support vector machines to predict electroluminescence of organic molecules [J] . ALANA FERNANDES GOLIN, RICARDO STEFANI Bulletin of Materials Science . 2013,第7期

机译：电致发光材料的定量结构-性质关系：人工神经网络和支持向量机预测有机分子的电致发光
4. Quantitative Structure-property Relationship Study for Estimating Flash Points of Organic Compounds Using Back-propagation Artificial Neural Networks [C] . PAN Yong, JIANG Juncheng International Symposium on Safety Science and Technology; 20061024-27; Changsha(CN) . 2006

机译：利用反向传播人工神经网络估算有机化合物闪点的定量构效关系研究
5. The development of quantitative structure-activity relationship models for physical property and biological activity prediction of organic compounds. [D] . Mattioni, Brian E. 2003

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6. Quantitative structure–activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks [O] . Saeed Yousefinejad, Marjan Mahboubifar, Rayhaneh Eskandari 2019

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Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review

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