Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Molecular Informatics Pub Date : 2024-11-01 Epub Date: 2024-10-15 DOI:10.1002/minf.202400082
Igor Baskin, Yair Ein-Eli
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引用次数: 0

Abstract

This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.

腐蚀科学的化学信息学:数据驱动的有机分子腐蚀抑制模型。
本文回顾了机器学习在有机分子腐蚀抑制方面的应用。考虑的方法包括定量结构-性质关系(QSPR)和相关的数据驱动方法。在将其应用于缓蚀时,考虑了其主要组成部分的特征,包括数据集、响应特性、分子描述符、机器学习方法和结构-特性模型。结果表明,决定其选择和应用特征的最重要因素是(1) 数据集的规模较小或非常小;(2) 与抑制剂分子在金属表面的吸附有关的缓蚀机制;(3) 响应特性的多因素调节和噪声。在此基础上,考虑了机器学习在铁、铝和镁基材料缓蚀方面的应用。讨论了 QSPR 和相关数据驱动缓蚀建模的主要发展趋势,指出了其不足之处和常见错误,并展望了其进一步发展的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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