QSAR model for predicting Henry's constant

Nirmalakhandan, Nagamany; Speece, Richard E.

doi:10.1021/es00176a016

Cited by 159 publications

(93 citation statements)

References 10 publications

(28 reference statements)

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“…Many semi-theoretical models describing the QSARs have been proposed to predict the sorption parameters including the octanol-water partitioning coefficient, the LSE model, (Nirmalakhandan and Speece, 1993;Luehrs et al, 1996), the MCI model (Blum et al, 1994;Nirmalakhandan and Speece, 1988), and the solvophobic theory (Belfort et al, 1984). While utilising the above QSARs, it is hypothesised that the sorption coefficients normalised to the organic carbon fraction of the sediment (K exp oc ) values for pollutants in fact depend upon the background ionic strength of the system and the validity of this approach has been demonstrated by incorporating the ionic strength term into the three QSARs namely octanol-water partitioning coefficient model (Kow model), the LSE model, and the MCI model.…”

Section: Estimation Of Sorption Coefficients Using Qsarsmentioning

confidence: 99%

“…Many semi-theoretical quantitative structure activity relationships (QSARs) have been proposed to parameterise sorption onto soils/sediments (Baker et al, 1997;Meylan et al, 1992), sorption onto activated carbon (Blum et al, 1994;Luehrs et al, 1996;Belfort et al, 1984), sorption onto wastewater solids (Dobbs et al, 1989), adsorption of organic vapours onto activated carbon (Urano et al, 1982;Nirmalakhandan and Speece, 1993;Prakash et al, 1994), dissolution of compounds (Kamlet et al, 1986;Lane and Loehrs, 1995;Speece, 1988, 1989;Dunnivent et al, 1992), Henry's law constants (Nirmalakhandan and Speece, 1988;Dunnivent et al, 1992;Nirmalakhandan et al, 1997;Brennan et al, 1998), chemical biotransfer (Dowdy et al, 1996), chemical toxicity (Blum and Speece, 1990;Xu and Nirmalakhandan, 1998); and octanolwater partition coefficients (Sablijic et al, 1993). It is well known that the physico-chemical interactions depend on chemical properties of the pollutant such as aqueous solubilities, octanol/water partition coefficients, or structural characteristics including substituted groups on the pollutant molecule, molar volume of the molecule, topology as well as the intrinsic properties like polarity and polarisability of the pollutant molecules (Westall et al, 1985;Kamlet et al, 1986).…”

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confidence: 99%

“…Lately, more attention has been given to applications of QSARs to predict sorption parameters at the soil or sediment interface (Meylan et al, 1992) because such parameterisation can help in a variety of situations including prediction of local and regional impact of aquatic environment, understanding relative importance of fate (Baker et al, 1997) and transport processes in aquatic systems such as oceans, rivers, aquifers, and lakes, remediation of soils, sediments, and aquifers, bioaccumulation, biomagnification, toxicity to life forms, and bioavailability. Some of the most commonly used QSARs for prediction of sorption capacities at the soil-water interface include the octanol-water partitioning coefficient model (Means et al, 1980;Karickhoff, 1981;Piwomi and Banerjee, 1989;Chiou, 1983;Schwarzenbach et al, 1993), the Linear Solvation Energy model (Nirmalakhandan and Speece, 1993;Luehrs et al, 1996), Molecular Connectivity Indices model (Blum et al, 1994;Nirmalakhandan and Speece, 1988;Xu and Nirmalakhandan, 1998); the Solvophobic Theory (Belfort et al, 1984); and the combination of polarisability parameter, molecular connectivity index, and hydrogen bonding index model (Nirmalakhandan et al, 1997). All these QSARs have essentially tried to focus on several complex physico-chemical surface interactions including adsorption, absorption, ion exchange, pore diffusion, redox reaction, precipitation, dissolution, acid/base hydrolysis, formation of co-ordinated complexes giving rise to the polynuclear species (Weber et al, 1991;Banerji et al, 1993;Gao et al, 1998).…”

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QSAR Models To Predict Effect of Ionic Strength on Sorption of Chlorinated Benzenes and Phenols at Sediment–Water Interface

Rao

Asolekar

2001

Water Research

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Abstract}It is hypothesised that the experimental sorption coefficient normalised to the organic carbon fraction of sediment (K exp oc ) for non-ionic, hydrophobic, organic pollutant depends upon the molecular properties as well as background ionic strength of the aquatic system. The utility of this concept has been demonstrated by incorporating ionic strength as a parameter in the three quantitative structure activity relationships (QSARs) namely octanol-water partitioning coefficient model (Kow model), the linear solvation energy model (LSE model), and molecular connectivity indices theory (MCI model). Four chlorinated benzenes and two chlorinated phenols were employed in the present study. Sorption experiments using sediment from the Patalganga River were conducted in laboratory (bottle point method) at different ionic strengths (viz. 0.01, 0.05, and 0.10 M). The K cal oc values predicted using Kow model incorporating ionic strength compare reasonably well with the K exp oc values (r 2 ¼ 0:60 and standard error of estimator i.e. SEE=0.35). The LSE model incorporating ionic strength too, was found to be equally good (r 2 ¼ 0:67, SEE=0.33). An attempt has also been made to validate the QSARs developed in the present study utilising the sorption parameters experimentally measured by Dewulf et al. (1996)

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Section: Estimation Of Sorption Coefficients Using Qsarsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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QSAR Models To Predict Effect of Ionic Strength on Sorption of Chlorinated Benzenes and Phenols at Sediment–Water Interface

Rao

Asolekar

2001

Water Research

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“…33 A further class of GC methods, based on solvent accessible surface areas (SASAs), has been proposed by Eisenberg and Malachlan 34 and improved by Hou et al 35 In the molecular-structure-based methods, molecular descriptors such as topological indices, quantum-chemical descriptors, geometrical and electrostatic descriptors, and so forth are used to encode (function f) the molecules. These methods were applied by Nirmalakhandan and co-workers [36][37][38] and by Russel and co-workers. 39 In both cases, a linear regression analysis is used to realize the mapping function g.…”

Section: Prediction Of the Solvation Free Energymentioning

confidence: 99%

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Bernazzani

Duce

Micheli

et al. 2006

J. Chem. Inf. Model.

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In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

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“…4,5 Brennan et al, 6 note that experimentally measured Henry's Law constants have been reported in the literature for fewer than 600 organic chemicals out of the over 70 000 chemicals that are in current use. Moreover, H values reported by different sources, for a given compound, can often differ by factors of up to 7.0, with an average variation of a factor of 2.4.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy ARTMAP‐Based Quantitative Structure—Property Relationship (QSPR) for the Henry′s Law Constant of Organic Compounds.

et al. 2003

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Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25°C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular connectivity index) were used as input parameters to the neural networks. Quantum chemical input descriptors included average molecular polarizability, dipole moments (total point charge, total hybridization, and total sum), ionization potential, and heat of formation. The fuzzy ARTMAP/QSPR correlated Henry's Law constant for -6.72 e logH e 2.87 with average absolute errors of 0.03 and 0.13 logH units for the overall data and the test set, respectively. The optimal 7-17-1 back-propagation/QSPR model was less accurate and exhibited larger average absolute errors of 0.28 and 0.27 logH units for the validation (recall) and test sets, respectively. The fuzzy ARTMAP-based QSPR was superior to the back-propagation and multiple linear regression/ QSPR models for Henry's Law constant of organic compounds.

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QSAR model for predicting Henry's constant

Cited by 159 publications

References 10 publications

QSAR Models To Predict Effect of Ionic Strength on Sorption of Chlorinated Benzenes and Phenols at Sediment–Water Interface

QSAR Models To Predict Effect of Ionic Strength on Sorption of Chlorinated Benzenes and Phenols at Sediment–Water Interface

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

A Fuzzy ARTMAP‐Based Quantitative Structure—Property Relationship (QSPR) for the Henry′s Law Constant of Organic Compounds.

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