Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

Admire, Brittany; Lian, Bo; Yalkowsky, Samuel H.

doi:10.1016/j.chemosphere.2014.10.031

Cited by 15 publications

(4 citation statements)

References 19 publications

(25 reference statements)

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“…In this number-based analysis, NH and CH 2 unified atoms have relative weights of approximately 0.5, so their α n -values are half as large as those of NH 2 and CH 3 unified atoms, making this analysis more like the ASA-based analysis. A similar pattern in relative weighting factors was observed in a number-based analysis of logarithms of activity coefficients . A concern about the physical significance of the number-based fit of Figure S12D (SI) is that α n -values quantifying interactions of urea with amide sp 2 C and amide sp 2 O are unusual.…”

Section: Analysis and Discussionsupporting

confidence: 66%

“…A similar pattern in relative weighting factors was observed in a number-based analysis of logarithms of activity coefficients. 45 A concern about the physical significance of the number-based fit of Figure S12D (SI) is that α n -values quantifying interactions of urea with amide sp 2 C and amide sp 2 O are unusual. In the ASA-based urea analysis (Table 1), α-values for these interactions are both very favorable, relative to interactions of urea with other atom types, and urea α-values for interactions of urea with amide sp 2 C and aromatic sp 2 C are approximately equal.…”

Section: Journal Of the American Chemical Societymentioning

confidence: 99%

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Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O

et al. 2017

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Quantitative information about amide interactions in water is needed to understand their contributions to protein folding and amide effects on aqueous processes, and to compare with computer simulations. Here we quantify interactions of urea, alkylated ureas and other amides by osmometry and amide-aromatic hydrocarbon interactions by solubility. Analysis of these data yields strengths of interaction of ureas and naphthalene with amide sp2O, amide sp2N, aliphatic sp3C, and amide and aromatic sp2C unified atoms in water. Interactions of amide sp2O with urea and naphthalene are favorable, while amide sp2O-alkyl urea interactions are unfavorable, becoming more unfavorable with increasing alkylation. Hence amide sp2O-amide sp2N interactions (proposed n–σ* hydrogen bond) and amide sp2O-aromatic sp2C (proposed n – π*) interactions are favorable in water, while amide sp2O-sp3C interactions are unfavorable. Interactions of all ureas with sp3C and amide sp2N are favorable and increase in strength with increasing alkylation, indicating favorable sp3C-amide sp2N and sp3C-sp3C interactions. Naphthalene results show that aromatic sp2C-amide sp2N interactions in water are unfavorable while sp2C-sp3C interactions are favorable. These results allow interactions of amide and hydrocarbon moieties and effects of urea and alkyl ureas on aqueous processes to be predicted or interpreted in terms of structural information. We predict strengths of favorable urea-benzene and N-methylacetamide interactions from experimental information to compare with simulations, and indicate how amounts of hydrocarbon and amide surface buried in protein folding and other biopolymer processes and transition states can be determined from analysis of urea and diethyl urea effects on equilibrium and rate constants.

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Section: Analysis and Discussionsupporting

confidence: 66%

Section: Journal Of the American Chemical Societymentioning

confidence: 99%

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O

et al. 2017

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“…Unfortunately, estimated K OW s for highly hydrophobic chemicals vary substantially across commonly used K OW estimation algorithms. For example, decabromodiphenyl ethane (BDEthane-209) has predicted K OW values spanning 6 orders of magnitude: log K OW of 7.72 (OPERA (https://comptox.epa.gov/ dashboard/)), 7.87 (Tetko et al, 2005), 8.70 (Wania and Dugani, 2003), 9.16 (Yue and Li, 2013), 9.68 (NICEATM via https://comptox.epa.gov/dashboard/), 10.56 (Admire et al, 2015)), 11.1 (ACD via http://www.chemspider.com/Chemical-Structure.82781.html (accessed 15:39, Apr 6, 2017)), 12.4 (Chemaxon via CSID:82781, http:// www.chemspider.com/Chemical-Structure.82781.html (accessed 15:39, Apr 6, 2017)), 12.3 (Carreira et al, 1994;Hilal et al, 1995), and 13.6 (US-EPA, 2012). From these estimates, BDEthane-209 is most likely highly hydrophobic with a log K OW of greater than 9.…”

Section: Introductionmentioning

confidence: 99%

Estimating n-octanol-water partition coefficients for neutral highly hydrophobic chemicals using measured n-butanol-water partition coefficients

et al. 2019

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Direct measurement of the n-octanol partition coefficients (K OW ) for highly hydrophobic organic chemicals is extremely difficult because of the extremely low concentrations present in the water phase. n-Butanol/water partition coefficients (K BW ) are generally much lower than K OW due to the increased solubility of solute in the alcohol saturated aqueous phase, and therefore become easier to measure. We measured the K BW for 25 neutral organic chemicals having measured log K OW s ranging from 2 to 9 and 4 additional highly hydrophobic chemicals, with unmeasured K OW s, having estimated log K OW s ranging from 6 to 18. The measured log K BW and log K OW values were linearly related, r 2 = 0.978, and using the regression developed from the data, K OW s were predicted for the 4 highly hydrophobic chemicals with unmeasured K OW s. The resulting predictions were orders of magnitude lower than those predicted by a variety of computational models and suggests the estimates of K OW in the literature for highly hydrophobic chemicals (i.e., log K OW greater than 10) are likely incorrect by several orders of magnitude.

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“…Methods to estimate T M have been developed over the past few decades yet only a few of these prediction methods are publicly and freely available in a user‐friendly format, e. g., EPI Suite™ Ver.4.11 . There are comparatively much fewer measured data for Δ S M , and while methods to predict Δ S M from chemical structure exist, the related calculation tools are either proprietary, e. g., SPARC or not publicly and freely available in a user‐friendly format, e. g., UPPER . Recognizing the emerging requirement for reliable and transparent QSARs and QSPRs, particularly in regulatory contexts, Paola Gramatica and colleagues advocated the need for standardized QSAR development and validation methods and reporting statistics to improve communication and understanding of QSAR predictions for the scientific and regulatory communities .…”

Section: Introductionmentioning

confidence: 99%

Application of an Iterative Fragment Selection (IFS) Method to Estimate Entropies of Fusion and Melting Points of Organic Chemicals

Brown¹,

Armitage

Arnot

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement. Abstract:The main objective of this study is to develop and evaluate novel Quantitative Structure-Property Relationships (QSPRs) for predicting entropy of fusion (DS M ) and melting point (T M ) of organic chemicals from chemical structure. The QSPRs are developed using the Iterative Fragment Selection (IFS) method that requires only 2D structural information from the user (SMILES codes) for property prediction. The QSPRs also provide information on the applicability domain for each calculation and uncer-tainty estimates for the predictions. The root mean square error (RMSE) for the external validation sets are 11.8 J mol À1 K À1 and 46.9 K for the DS M and T M QSPRs, respectively. The performance of the new QSPRs is comparable to other predictive methods but has advantages with respect to availability and ease of use as well as the guidance on applicability domain for each prediction. Limitations of the new QSPRs are discussed. The QSPRs are coded as a user-friendly, freely available tool.

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Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

Cited by 15 publications

References 19 publications

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O

Estimating n-octanol-water partition coefficients for neutral highly hydrophobic chemicals using measured n-butanol-water partition coefficients

Application of an Iterative Fragment Selection (IFS) Method to Estimate Entropies of Fusion and Melting Points of Organic Chemicals

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Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

Cited by 15 publications

References 19 publications

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H2O

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H2O

Estimating n-octanol-water partition coefficients for neutral highly hydrophobic chemicals using measured n-butanol-water partition coefficients

Application of an Iterative Fragment Selection (IFS) Method to Estimate Entropies of Fusion and Melting Points of Organic Chemicals

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Product

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Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O

Experimental Atom-by-Atom Dissection of Amide–Amide and Amide–Hydrocarbon Interactions in H₂O