Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Jäntschi, Lorentz; Bolboacă, Sorana D.

doi:10.1002/qua.21292

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Therefore, the MDF methodology proved able to provide valid and reliable MDF QSAR/QSPR models. Furthermore, the MDF models demonstrated statistically significant higher goodness-of-fit compared to previous models reported in the literature (confirmed by the correlated correlation analysis: Steiger's test [53], significance level of 5%) (19654 set [115], 22583 set [116], 23159 set [117], 26449 set [118], IChr10 set [27], MR10 set [119], PCB_rrf set [120], PCB_lkow set [121], RRC_lkow set [122], Tax385 & Tox395 [123], 41521 set [124], Triazines [125], 52344 set [126], 23151 set [127], 408462 set [128], 408464 set [129], 52730 set [130], cqdmdfv set [28]). The metaheuristic search was as good as the complete search and had the advantage of smaller costs in terms of resources and time [66,68,70].…”

Section: Resultssupporting

confidence: 56%

Molecular Design and QSARs/QSPRs with Molecular Descriptors Family

Bolboacă¹,

Jäntschi²,

Diudea

2013

CAD

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The aim of the present paper is to present the methodology of the molecular descriptors family (MDF) as an integrative tool in molecular modeling and its abilities as a multivariate QSAR/QSPR modeling tool. An algorithm for extracting useful information from the topological and geometrical representation of chemical compounds was developed and integrated to calculate MDF members. The MDF methodology was implemented and the software is available online (http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/). This integrative tool was developed in order to maximize performance, functionality, efficiency and portability. The MDF methodology is able to provide reliable and valid multiple linear regression models. Furthermore, in many cases, the MDF models were better than the published results in the literature in terms of correlation coefficients (statistically significant Steiger's Z test at a significance level of 5%) and/or in terms of values of information criteria and Kubinyi function. The MDF methodology developed and implemented as a platform for investigating and characterizing quantitative relationships between the chemical structure and the activity/property of active compounds was used on more than 50 study cases. In almost all cases, the methodology allowed obtaining of QSAR/QSPR models improved in explanatory power of structure-activity and structure-property relationships. The algorithms applied in the computation of geometric and topological descriptors (useful in modeling physicochemical or biological properties of molecules) and those used in searching for reliable and valid multiple linear regression models certain enrich the pool of low-cost low-time drug design tools.

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Section: Resultssupporting

confidence: 56%

Molecular Design and QSARs/QSPRs with Molecular Descriptors Family

Bolboacă¹,

Jäntschi²,

Diudea

2013

CAD

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“…Thus, Structure-Property-Activity Relationships (SPARs) must deal with certainties (such as molecular topology), uncertainties (such as molecular geometry), relativities (such as biological activities) and evidences (such as physical and chemical quantitative properties). The Molecular Descriptors Family (MDF) is an original structure-based approach [5] which generates for given structure(s) a huge pool of quantum based [6] descriptors of structure (indices) using a unitary methodology [7] that incorporated both topological and geometrical approaches. SPARs MDF methodology [8] uses a genetic algorithm [9] in order to obtain so called MDF-SPARs (structure-property or structure-activity relationships with Molecular Descriptors Family members relating the structure …”

mentioning

confidence: 99%

E-learning and e-evaluation: A Case Study

Jäntschi

Bolboacă

Marta

et al. 2008

2008 Conference on Human System Interactions

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Structure/Activity/Property Relationships (SARs, SPRs, and PARs) appears with the studies of Louis Plack HAMMETT in 1937 [1]. The most important applications of Hammett's equation were summarized in [2].Quantitative relationships (QSAR, QSPR, QPAR) occur when the property/activity is quantitative. Not all properties and activities of chemical compounds can be classified as quantitative. In fact, few properties meet all theoretical requirements to be quantitative [3]. From this reason in the last time are avoided to be used QSAR, QSPR, and QPAR, in their place being used (Q)SAR, (Q)SPR, and (Q)PAR, or more simple SAR, SPR, and PAR. Structure-based approaches have two levels (topological and geometrical). In the topological based level, an atom, a bond from a molecule can exist (and then are evidenced through electronic transitions and/or molecular vibrations and/or rotations) or not (being a matter of 0 and 1). Not so simple stays things related to molecular geometry (especially on liquid or gas phases). Heisenberg uncertainly principle [4] shows the uncertainly rules presented at micro level (molecular and atomic level). More than that, molecular geometry depends on the environment where the molecule is (vicinity of the molecule), temperature, pressure, so on, thus dealing with molecular geometry is both a matter of relativity and a matter of uncertainty. Thus, Structure-Property-Activity Relationships (SPARs) must deal with certainties (such as molecular topology), uncertainties (such as molecular geometry), relativities (such as biological activities) and evidences (such as physical and chemical quantitative properties MATERIALS:The hydrophobicity on Hessa et al. scale [ 10 ] of fifteen standard amino acids was the property of interest.The experimental values of hydrophobicity were as follows: alanine (0.11), asparagine (2.05), aspartate (3.49), cysteine (-0.13), glutamine (2.36), glutamate (2.68), glycine (0.74), isoleucine (-0.6), leucine (-0.55), lysine (2.71), methionine (-0.1), phenylalanine (-0.32), serine (0.84), threonine (0.52), and valine (-0.31). MDF-SPAR completion: MDF Calculator & MDF Predictor. RESULTS:The model with one and two descriptors, respectively proved to has estimated and predictive abilities: Ŷ mono =-0.58+iMDRoQg·8.53 Eq(1) Ŷ bi =-1.36+iMDRoQg·6.03+ISPDwQg·0.08 Eq(2) The application of the parameters presented in the table bellow leads to the results presented bellow: Param.Eq (1) Eq (2) o building (with HyperChem) of topological (2D) and geometrical (3D) through same choices as were build the selected set to obtain predicted value(s) for the property / activity of the new compounds, even if this (these) compound (s) were not yet synthesized, in order to see if the new structure (virtual compound at this time) has or not improvements in desired property/activity. CONCLUSION MDF method and MDF-SAR methodology proved to be a very good tool for design of chemical compounds.(6) MDF Calculator (7) MDF Predictor ACKNOWLEDGEMENTS: [MDF] The MDF project was supported through ET36 resear...

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Evaluation of Phenolic Compound Toxicity Using a Bioluminescent Assay with the Fungus Gerronema viridilucens

Ventura

Mendes

Oliveira

et al. 2020

Environmental Toxicology and Chemistry

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Basidiomycetes (phylum Basidiomycota) are filamentous fungi characterized by the exogenous formation of spores on a club‐shaped cell called a basidium that are often formed on complex fruiting bodies (mushrooms). Many basidiomycetes serve an important role in recycling lignocellulosic material to higher trophic levels, and some show symbiotic relationships with plants. All known bioluminescent fungi are mushroom‐forming basidiomycetes in the order Agaricales. Hence, the disruption of the basidiomycete community can entirely compromise the carbon cycle in nature from fungi to higher trophic levels. The fungus Gerronema viridilucens was used in the present study to investigate the toxicity of a phenolic compound series based on the inhibition of its bioluminescence. The median effect concentration (EC50) obtained from curves of bioluminescence inhibition versus log [phenolic compound] showed that 2,4,6‐trichlorophenol was the most toxic compound in the series. The log EC50 values of all phenolic compounds were then used for the prediction of their toxicity. The univariate correlation of log EC50 values obtained from 6 different phenolic compounds was stronger with the dissociation constant (pKa) than with 1‐octanol/water partition coefficient (KOW). Nevertheless, the toxicity can be better predicted by using both parameters, suggesting that the phenol‐driven uncoupling of fungus mitochondrial adenosine triphosphate synthesis is the origin of phenolic compound toxicity to the test fungus. Environ Toxicol Chem 2020;39:1558–1565. © 2020 SETAC

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Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Cited by 6 publications

References 12 publications

Molecular Design and QSARs/QSPRs with Molecular Descriptors Family

Molecular Design and QSARs/QSPRs with Molecular Descriptors Family

E-learning and e-evaluation: A Case Study

Evaluation of Phenolic Compound Toxicity Using a Bioluminescent Assay with the Fungus Gerronema viridilucens

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