Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials′ Endpoints

Toropova, Alla P.; Toropov, Andrey A.; Veselinović, Aleksandar M.; Veselinović, Jovana B.; Leszczyńska, Danuta; Leszczyński, Jerzy

doi:10.1016/b978-0-08-101129-4.00008-4

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…The prevalence of each feature in the active training (ATRN) and calibration (CAL) sets may be used to determine the quality of the split. 53–56 where, P ( A k ) and P ′( A k ) is the probability of attribute A k in the ATRN set and CAL set, respectively and is calculated as .…”

Section: Methodsmentioning

confidence: 99%

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

et al. 2022

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“…As noted in the previous section, the simultaneous examination of two endpoints is an attractive way in the QSPR/QSAR analysis. In addition to multi-target QSAR, the similarity of endpoints may be a heuristic tool of control of the biochemical knowledge [105][106][107]. Similarity/dissimilarity of endpoints can be expressed via correlation weights of molecular features extracted from SMILES [105].…”

Section: Similarity Of Endpointsmentioning

confidence: 99%

“…The development of criteria of the predictive potential of models (Table 3) also is a part of the "intensive" studies. Maybe, search for the similarity of endpoints [105][106][107], also, will become part of "intensive" QSPR/QSAR researches.…”

Section: The Simplicity or The Efficiency: Which Is Better?mentioning

confidence: 99%

QSPR/QSAR: State-of-Art, Weirdness, the Future

Toropov

Toropova

2020

Molecules

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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“…Since their discovery in the 20th century, carbon nanomaterials have attracted a great deal of interest in the scientific community, stemming mainly from their wide variety of applications. In particular, single-walled carbon nanotubes (SWCNTs), due to their flexible nature and versatility with respect to chemical functionalization, are being used in new nanomedicine applications, such as active ingredients and pharmaceutical excipients for the design of several drug delivery systems. , …”

Section: Introductionmentioning

confidence: 99%

“…The main challenge for the safe use of these nanomaterials is the lack of data about their potential nanotoxicity (mitochondrial channel toxicity induced by SWCNTs), a challenge that has prompted the use of modeling tools that can make effective use of smaller data sets in the first instance. In this context, chemoinformatic methods may be also very useful for predicting structure–property relationships for the carbon nanotubes or other nanomaterials . With regard to this, the so-called quantitative structure–binding relationships (QSBRs) are used for models that employ ligand–protein docking interaction data, and by analogy, in particular for the case of nanomaterials, these are called nano-QSAR (NQSBR) models.…”

Section: Introductionmentioning

confidence: 99%

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1)

González-Durruthy

Monserrat

Oliveira

et al. 2019

Chem. Res. Toxicol.

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We present an in silico approach for modeling the noncovalent interactions between the human mitochondrial voltage-dependent anion channel (hVDAC1) and a family of single-walled carbon nanotubes (SWCNTs) with a defined pattern of topological vacancies (v = 1–16), obtained by removing atoms from the SWCNT surface. The general results showed more stable docking interaction complexes (SWCNT–hVDAC1), with more negative Gibbs free energy of binding affinity values, and a strong dependence on the vacancy number (R 2 = 0.93) and vacancy formation energy (R 2 = 0.96). In addition, for most of the SWCNT vacancies that were analyzed, the interatomic distances for the interactions of the SWCNT–hVDAC1 complex with the functional catalytic residues (i.e., Pro7, Gln199, Gln182, Phe181, Val20, Asp19, Lys15, Gly14, Asp12, Ala11, and Arg18) that form the hVDAC1 active site (i.e., the voltage-sensing N-terminal α-helix segment) were very similar to or shorter than the interatomic distances of these residues for ATP–hVDAC1 interactions. In particular, the hVDAC1 residues that can be phosphorylated like Tyr10, Tyr198, and Se16 were significantly perturbed by the interactions with SWCNT with at least nine vacancies. In addition, the SWCNT vacancy family members can affect the flexibility properties of the hVDAC1 N-terminal α-helix segment inducing different patterns of local perturbations in inter-residue communication. Finally, vacancy quantitative structure–binding relationships (V-QSBRs) were unveiled for setting up a robust model that can predict the strength of docking interactions between SWCNTs with a specific topological vacancy and hVDAC1. The developed V-QSBR model classified properly all of the SWCNTs with a different number of SWCNT vacancies with exceptional sensitivity and specificity (both equal to 100%), indicating a strong potential to unequivocally predict the influence of SWCNT vacancies on the mitochondrial channel interactions.

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Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials′ Endpoints

Cited by 6 publications

References 33 publications

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

QSPR/QSAR: State-of-Art, Weirdness, the Future

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1)

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