Development and uniqueness test of highly selective atomic topological indices based on the number of attached hydrogen atoms

IF 2.7 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Journal of molecular graphics & modelling Pub Date : 2024-03-06 DOI:10.1016/j.jmgm.2024.108752
Yue Luan , Xianlan Li , Dingling Kong , Wanli Li , Wei Li , Qingyou Zhang , Aimin Pang
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Abstract

On the basis of the atomic graph-theoretical index – aEAID (atomic Extended Adjacency matrix IDentification) and molecular adjacent topological index – ATID (Adjacent Topological IDentification) suggested by one of the authors (Zhang Q), a highly selective atomic topological index - aATID (atomic Adjacent Topological IDentification) index was suggested to identify the equivalent atoms in this study. The aATID index of an atom was derived from the number of the attached hydrogen atoms of the atom but omitting bond types. In this case, the suggested index can be used to identify equivalent atoms in chemistry but perhaps not equivalent in the molecular graph. To test the uniqueness of aATID indices, the virtual atomic data sets were derived from alkanes containing 15–20 carbon atoms and the isomers of Octogen, as well as a real data set was derived from the NCI database. Only four pairs of atoms from alkanes containing 20 carbons can't be discriminated by aATID, that is, four pairs of degenerates were found for this data set. To solve this problem, the aATID index was modified by introducing distance factors between atoms, and the 2-aATID index was suggested. Its uniqueness was examined by 5,939,902 atoms derived from alkanes containing 20 carbons and further 16,166,984 atoms from alkanes of 21 carbons, and no degenerates were found. In addition, another large real data set of 16,650,688 atoms derived from the PubChem database was also used to test the uniqueness of both aATID and 2-aATID. As a result, each atom was successfully discriminated by any of the two indices. Finally, the suggested aATID index was applied to the identification of duplicate atoms as data pretreatment for QSPR (Quantitative Structure–Property Relationships) studies.

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基于附着氢原子数量的高选择性原子拓扑指数的开发和唯一性测试
本研究在作者之一(Zhang Q)提出的原子图论指数--aEAID(原子扩展邻接矩阵识别)和分子邻接拓扑指数--ATID(邻接拓扑识别)的基础上,提出了一种高选择性的原子拓扑指数--aATID(原子邻接拓扑识别)来识别等价原子。原子的 aATID 索引是根据原子所附氢原子的数量得出的,但忽略了原子键的类型。在这种情况下,建议的索引可用于识别化学中的等效原子,但在分子图中可能并不等效。为了检验 aATID 指数的唯一性,虚拟原子数据集来自含 15-20 个碳原子的烷烃和辛原异构体,真实数据集来自 NCI 数据库。在含 20 个碳原子的烷烃中,只有四对原子不能被 aATID 识别,即在该数据集中发现了四对退化原子。为了解决这个问题,我们对 aATID 指数进行了修改,引入了原子间距离因子,并提出了 2-aATID 指数。对来自含 20 个碳原子的烷烃中的 5,939,902 个原子和来自含 21 个碳原子的烷烃中的 16,166,984 个原子进行了检验,结果没有发现退化现象。此外,我们还使用了另一个来自 PubChem 数据库的 16,650,688 个原子的大型真实数据集来测试 aATID 和 2-aATID 的唯一性。结果,每个原子都能成功地被这两个指数中的任何一个区分出来。最后,建议的 aATID 指数被用于识别重复原子,作为 QSPR(定量结构-性质关系)研究的数据预处理。
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来源期刊
Journal of molecular graphics & modelling
Journal of molecular graphics & modelling 生物-计算机:跨学科应用
CiteScore
5.50
自引率
6.90%
发文量
216
审稿时长
35 days
期刊介绍: The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.
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