Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives

Sizochenko, Natalia; Кузьмин, В. Е.; Ognichenko, L. N.; Leszczyński, Jerzy

doi:10.1007/s10910-015-0581-8

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…It should be noted that vertices 1 and 2 for C 32 , as well as vertices 7,8,9,10,13,14,43,44,45,46,49, 50 for C 78 are achiral (marked as Ø is following tables) because there are at least two topologically equivalent terminal vertices in respective simplexes (Fig. 8).…”

Section: Results: the Assessment Of Equivalence/nonequivalence Of Thementioning

confidence: 99%

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Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

et al. 2016

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In this study, the topological informational field was applied for the assessment of equivalence or nonequivalence of atoms and molecular fragments in structurally homogeneous chiral fullerenes. Two types of so-called topological informational potentials were calculated using noniterative heuristic method. Application of methods that allow estimating the nonequivalence of similar atoms could be a breakthrough for the description of highly homogenous structures. It was shown that method, which takes into account multiple paths in the molecular graph, is the most efficient. Results of equivalence/nonequivalence analysis allow to differentiate sources of molecule's chirality.

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Section: Results: the Assessment Of Equivalence/nonequivalence Of Thementioning

confidence: 99%

“…In this case, IF reflects information about the distribution of the target property in space. Nowadays, IF theory was utilized as a powerful descriptive tool for "structure-property" tasks [3,4,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

et al. 2016

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“…The eclectic data are translated into optimal nano-descriptors (the sum of weights of quasi-SMILES) for the outcome prediction and Monte Carlo optimization is used to select the optimal descriptors. Optimal-based SMILES descriptors can be calculated with the International Chemical Identifier (https://iupac.org/who-we-are/divisions/division-details/inchi/ (Webpage accessed autumn 2019)) even though, as noted by References [84,85], SMILES-based descriptors can have some drawbacks for describing endpoints for some NMs and for the interpretability of the models. To overcome the limitations of optimal-SMILES, the Improved SMILES-Based Optimal Descriptors has been proposed [84] as a novel descriptor characterizing structural and chemical properties, which interprets the endpoint more accurately.…”

Section: Molecular Structures' Codificationmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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“…Several studies,experimental as well as computational, are known in the literature to analyze the solubility of fullerenes, but mainly available for the solubility of C 60 fullerenes in diverse solvents . The in‐silico (computational) studies have an advantage over the in‐vivo and in‐vitro (experimental) studies as the former are cost effective, less laborious, and can be analyzed simultaneously for diverse solvents.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Solubility of C₇₀ Fullerenes in Diverse Solvents: Role of Quantum‐mechanical Descriptors

Lata

Vikas

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: Solubility of fullerenes in diverse solvents is difficult to model owing to the involvement of inherent quantum-mechanical interactions. This work proposes pure quantum mechanical models for predicting the solubility of C 70 fullerenes. The models are developed using singlemolecule quantum mechanical properties of solvents,which includes electron-correlation based descriptors accounting for the instantaneous inter-electronic interactions responsible for the origin of these properties. The external predictivity of the models was validated using an external prediction set of compounds not exposed to the model during its development. The most influential factor affecting the solubility was found to be electron-correlation contribution to the energy of highest occupied molecular orbital of solvents, which in fact, has been analyzed to suppress the solubility of fullerenes. However, the polarizability of the solvents, particularly that due to the quantum-mechanical exchange interactions between the electrons, is observed to drive the solubility of C 70 fullerenes. The present work also found several other quantum mechanical properties affecting the solubility of fullerenes, which pay valuable insights towards the origin of the solubility of carbon-based nanomaterials.Keywords: modelling · solubility · fullerenes · quantum-mechanical methods Recently, Gupta and Basant, [25] using the DTB method, has also attempted to model the solubility of C 70 fullerenes. Very recently, Torpova and Torpov, [26] has proposed nano-QSPRs based on a quasi-simplified molecular input-line entry systems (SMILES) which is an expansion of the traditional SMILES but under varying conditions. The aim of the present work is to explore the correlations between the solubility of C 70 fullerenes in diverse solvents and the single-molecule quantum-mechanical properties of these solvents. For this, the quantitative models based on the quantum-mechanical descriptors are [a] S. Lata, Vikas

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Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives

Cited by 11 publications

References 25 publications

Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

Practices and Trends of Machine Learning Application in Nanotoxicology

Modeling the Solubility of C₇₀ Fullerenes in Diverse Solvents: Role of Quantum‐mechanical Descriptors

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Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives

Cited by 11 publications

References 25 publications

Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

Efficacy of topological informational potentials for analysis of nonequivalent atoms in molecular graphs: the case of chiral fullerenes

Practices and Trends of Machine Learning Application in Nanotoxicology

Modeling the Solubility of C70 Fullerenes in Diverse Solvents: Role of Quantum‐mechanical Descriptors

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Modeling the Solubility of C₇₀ Fullerenes in Diverse Solvents: Role of Quantum‐mechanical Descriptors