Graph theory in structure–property correlations

Vinogradova, M. G.; Fedina, Yulia A.; Papulov, Yu. G.

doi:10.1134/s0036024416020345

Cited by 4 publications

(7 citation statements)

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“…There is a great resemblance between a graph and structural formula, as depicted in Figure 1, in which a direct correlation among vertices and atoms, as well as edges and chemical bonds, is clearly perceived. Unfortunately, a critical drawback precludes a more generalized use of the graph theory in chemistry-the incapability to distinguish atoms or bonds [11][12][13][14][15][16][17][18][19][20][21][22]. The quantification of specific interactions among substructural fragments is crucial to infer any molecular property and, to do so, it is crucial to differentiate atoms and chemical bonds, as stated previously.…”

Section: Introductionmentioning

confidence: 99%

“…The inability to differentiate atoms or bonds on the graph theory is thwarted while using SMILES through the correspondence of each 3 of 19 substructural fragment that was obtained from the graph theory with the atom connection pattern obtained from the input data. Structural chemical data, such as a mol file or similar, define different types of chemical bonds that are used to connect different atoms and such information is transferred precisely to the SMILES notation [17][18][19][20][21][22][23][24]. While using such an approach, it is possible to discriminate any atom, even its hybridization, as well as its precise location into the molecule.…”

Section: Introductionmentioning

confidence: 99%

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Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR

et al. 2020

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Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR

et al. 2020

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“…Методика изучения корреляций «структура -свойство» с помощью топологического подхода нами была разработана ранее [1][2][3][4][5][6][7].…”

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Графические Зависимости В Исследовании Корреляций Структура – Свойство Карбоновых Кислот

Виноградова,

Барсукова

2023

Вестник Тверского Государственного Университета. Серия: Химия

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Построены и проанализированы графические зависимости «Свойствотопологический индекс (ТИ)», «Свойство -номер изомера» и «Топологический индекс -номер изомера». Показано, что в одних случаях энтальпия образования и теплоёмкость хорошо коррелируют с ТИ, а в других случаях такой зависимости нет.

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“…Таким образом, в молекулярном графе (МГ) вершины соответствуют атомам, а рёбра -химическим связям [1][2][3][4][5][6][7]. При этом учитываются только скелетные атомы, а графы гетероядерных систем имеют разнотипные вершины и различающиеся рёбра (рис.1).…”

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“…С увеличением числа изомеров корреляции между свойством Р и топологическим индексом усложняются. Это необходимо учитывать при аналитическом представлении зависимостей "Свойство вещества Р -ТИ графа молекулы" [1][2][3][4][5][6][7].…”

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Graphic Dependences in the Study of Structure - Properties of Glycols

Vinogradova¹,

Козлова²

2021

Вестник Тверского Государственного Университета. Серия: Химия

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Обсуждается взаимосвязь энтальпии образования двухатомных спиртов с различными факторами химического строения. Построены и проанализированы графические зависимости «Энтальпия образования - топологический индекс (ТИ)», «Энтальпия образования - номер изомера» и «Топологический индекс - номер изомера». Показано, что в одних случаях энтальпия образования хорошо коррелирует с ТИ, а в других случаях такой зависимости нет. The relationship between the enthalpy of formation of dihydric alcohols and various factors of chemical structure is discussed. Graphical dependencies "Enthalpy of formation - topological index (TI)", "Enthalpy of formation - isomer number" and "Topological index - isomer number" were constructed and analyzed. It was shown that in some cases the enthalpy of formation correlates well with TI, while in other cases there is no such dependence.

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Graph theory in structure–property correlations

Cited by 4 publications

References 1 publication

Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR

Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR

Графические Зависимости В Исследовании Корреляций Структура – Свойство Карбоновых Кислот

Graphic Dependences in the Study of Structure - Properties of Glycols

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