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The effect of different log P algorithms on themodeling of the soil sorption coefficient of nonionicpesticides

Ralpho Rinaldo dos Reis a,b, Silvio Cesar Sampaio a,Eduardo Borges de Melo b,*aGraduate Program in Agricultural Engineering (PGEAGRI), Western Parana State University (Universidade

Estadual do Oeste do Parana, UNIOESTE), Cascavel, Parana, BrazilbTheoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), UNIOESTE, Cascavel, Parana, Brazil

a r t i c l e i n f o

Article history:

Received 18 January 2013

Received in revised form

26 April 2013

Accepted 27 June 2013

Available online xxx

Keywords:

Environmental risks

Models

Hydrophobic parameter

Soil contamination

QSPR

* Corresponding author. Dept of Pharmacy, 2E-mail address: eduardo.b.de.melo@gmai

Please cite this article in press as: dos Reiscoefficient of nonionic pesticides, Water

0043-1354/$ e see front matter ª 2013 Elsevhttp://dx.doi.org/10.1016/j.watres.2013.06.053

a b s t r a c t

Collecting data on the effects of pesticides on the environment is a slow and costly process.

Therefore, significant efforts have been focused on the development of models that predict

physical, chemical or biological properties of environmental interest. The soil sorption

coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in

environmental risk assessments. Thus, several log Koc prediction models that use the hy-

drophobic parameter log P as a descriptor have been reported in the literature. Often, al-

gorithms are used to calculate the value of log P due to the lack of experimental values for

this property. Despite the availability of various algorithms, previous studies fail to

describe the procedure used to select the appropriate algorithm. In this study, models that

correlate log Koc with log Pwere developed for a heterogeneous group of nonionic pesticides

using different freeware algorithms. The statistical qualities and predictive power of all of

the models were evaluated. Thus, this study was conducted to assess the effect of the log P

algorithm choice on log Koc modeling. The results clearly demonstrate that the lack of a

selection criterion may result in inappropriate prediction models. Seven algorithms were

tested, of which only two (ALOGPS and KOWWIN) produced good results. A sensible choice

may result in simple models with statistical qualities and predictive power values that are

comparable to those of more complex models. Therefore, the selection of the appropriate

log P algorithm for modeling log Koc cannot be arbitrary but must be based on the chemical

structure of compounds and the characteristics of the available algorithms.

ª 2013 Elsevier Ltd. All rights reserved.

1. Introduction is incompatible with the current way of life. As a result of this

In modern society, chemicals make important contributions

to the quality of life. Chemical compounds, including pesti-

cides, drugs and solvents, are found in virtually all sectors of

economic activity. Thus, the ideal of a world free of chemicals

069 Universitaria St., 858l.com (E.B. de Melo).

, R.R., et al., The effect ofResearch (2013), http://d

ier Ltd. All rights reserved

widespread use, concern regarding the final destination of and

the health and environmental risks associated with these

substances is increasing in certain segments of society

(Mackay and Webster, 2003). Therefore, studies that collect

data on chemical properties are needed to plan the recovery of

19-110 Cascavel, PR, Brazil. Tel.: þ55 45 32203256.

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

.

Nomenclature

ARE Average relative error (%)

Co Concentration in the organic phase (mol L�1)

Cs Concentration in soil (mg kg-1)

Cw Concentration in the aqueous phase (mg L�1 and

mol L�1)

F F statistic

k Slope of the regression line without intercept

Kd Linear soil sorption coefficient (L kg�1)

Koc Soil sorption coefficient normalized to organic

carbon content (L kg�1)

LOO Leave-one-out validation

LNO Leave-N-out validation

P Octanol/water partitioning coefficient

PRESScal Residual sum of squares of the calibration

PRESSev Residual sum of squares of the external validation

PRESSval Residual sum of squares of the cross validation

Q2LNO Coefficient of determination for the leave-N-out

validation

Q2LOO Coefficient of determination for the leave-one-out

validation

QSPR Quantitative StructureeProperty Relationships

R2 Coefficient of determination

R20 Coefficient of determination of the regression line

without intercept

r2m Modified coefficient of determination

R2PRED Coefficient of determination of the external

validation

S Solubility in water (mol L�1)

SEC Standard error of calibration

SEV Standard error of the validation

SEP Standard error of the prediction

wa t e r r e s e a r c h x x x ( 2 0 1 3 ) 1e92

contaminated areas or the preservation of sites not yet

degraded.

Pesticides are widely used in agriculture to increase yields

and cropquality anddecrease labor and energy costs. However,

only a small fraction (approximately 0.1%) of the pesticides

applied in farming reach their targets. The remainder tends to

move through the environment and may thus contaminate

surface and groundwater (Arias-Estevez et al., 2008).

The soil sorption coefficient normalized to organic carbon

content (Koc) is a key physicochemical parameter that can be

used to determine the mobility and the final destination of

chemicals released into the environment. This coefficient is

expressed by the following equation:

Koc ¼ Kd=OC ¼ Cs=ðCw � OCÞCs and Cw are respectively the concentration of the chemical

component sorbed by the soil (mg kg-1) and the concentration

in the surrounding aqueous phase (mg L�1), OC is organic

carbon content in units of g OC/g dry soil, and Kd is the linear

sorption coefficient (Doucette, 2003). The smaller Koc value of a

given compound is, the higher its soil mobility and the higher

its potential to contaminate groundwater (Dearden, 2002;

Arias-Estevez et al., 2008).

The Koc coefficient value of a chemical can be experimen-

tally measured using the batch equilibrium (ASTM, 2001) or

the soil column (Lee et al., 1991) methods. The extent of the

sorption of a chemical is determined by the chemical’s mo-

lecular structure but also depends on factors related to soil

characteristics, including particle size, porosity, pH and

organic carbon content (Dearden, 2002). Organic carbon con-

tent is a determining factor in the sorption of nonionic com-

pounds. Thus, the hydrophobicity of these compounds is the

driving force of the soil sorption process (Wen et al., 2012).

Hydrophobic interaction is the main mechanism in the sorp-

tion of nonionic compounds, whereas for more polar com-

pounds, hydrophilic interactions are important. In these

circumstances, the interactions of clays and silica with the

polar functional groups of these substances become signifi-

cant. It is important to note that Kd varies from soil to soil and

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

is usually normalized when determining Koc because, for

many compounds such as hydrophobic organics, sorption is

directly proportional to the organic carbon content. Thus,

when OC > 0.001 and the chemical is nonionic or neutral, Koc

can be considered a constant for a given chemical. Koc reflects

the fact that soil organic carbon is the major sorption domain

for hydrophobic compounds. However, the Koc approach may

not be suitable when soils have low organic carbon contents

or high clay contents and may not be applicable for chemicals

that have highly polar functional groups that significantly

interact with polar or charged sites on soil surfaces (Doucette,

2003; Wen et al., 2012). A further detailed description of

intermolecular interactions involved in the soil sorption pro-

cess of organic compounds can be found in the studies of von

Oepen et al. (1991) and Allen-King et al. (2002).

The need for a complete, reliable and updated database of

Koc values has led to the development of a large number of

models to predict this parameter (Gramatica et al., 2000).

Several quantitative structureeproperty relationship (QSPR)

studies, based on the molecular structure of chemical com-

pounds, have been published in the last decades. Thus,

various descriptors, including physicochemical properties,

chromatographic factors, steric factors, linear solvation en-

ergy relationships (LSER), topological indices and quantum

parameters have been used in Koc modeling (Gawlik et al.,

1997; Muller, 1997; Doucette, 2003; Huuskonen, 2003; Nguyen

et al., 2005; Gramatica et al., 2007; Razzaque and Grathwohl,

2008; Goudarzi et al., 2009; Wen et al., 2012). However, most

QSPR models were designed using small datasets and are

class-specific or have not been properly validated. The non-

validation of a prediction model precludes its application

given the lack of knowledge on its predictive power. There-

fore, to be considered reliable, a model must be properly

validated (Kiralj and Ferreira, 2009; Chirico and Gramatica,

2011; Roy et al., 2012).

Manymodels have been based on the relationship between

log Koc and the logarithmof water solubility (log S; S inmol L�1)

or the logarithm of octanol/water partition coefficient (log P)

(Gawlik et al., 1997; Doucette, 2003; Razzaque and Grathwohl,

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Table 1 e Chemical classes of pesticides used in the

wat e r r e s e a r c h x x x ( 2 0 1 3 ) 1e9 3

2008). This important partition coefficient is given by the

equation:

P ¼ Co=Cw

Co and Cw are the concentrations of the compound in the

organic and aqueous phases of an octanol/water system at

equilibrium.

The strong correlation between these two descriptors (log P

and log S ) indicates that such models may be considered

identical or parallel. The log P parameter is a satisfactory

descriptor for modeling the soil sorption coefficient and has

been used in QSPR studies for various classes of organic

compounds, including pesticides (Lohninger, 1994; Sabljic

et al., 1995). The use of log P in soil sorption modeling is

common in uni- and multivariate models. Baker et al. (2000)

demonstrated that the relationship between log Koc and log P

deviates from linearity for compounds with log P > 5. Wen

et al. (2012) proposed a nonlinear model based on the rela-

tionship between log Koc and log P for a heterogeneous set of

594 organic compounds but also noted the existence of line-

arity in the log P range from 0.5 to 7.5.

The use of experimental log P values enables researchers to

obtain further realistic QSPRmodels. However, inmany cases,

the experimental data are not available, and the assessment

of these values can be extremely laborious and consume

substantial time and resources. These circumstances, com-

bined with the large number of new natural and synthesized

substances, complicate the creation of an updated database.

Thus, computational approaches to log P calculation are

valuable tools in QSPR studies (Melo and Ferreira, 2009).

Currently, there are several commercial and freeware algo-

rithms available for calculating log P. The two main methods

used for this calculation are (a) the fragment- or atom-type-

based substructure method and (b) the whole-molecule

method, which is based on molecular properties (Mannhold

and van de Waterbeemd, 2001).

QSPR studies on the soil sorption coefficient simply omit

how or why a given algorithmwas chosen for calculating log P

or whether any objective criterion with respect to the choice

ensures that the best regressionmodel is generated. Thus, the

aim of this study was to assess different freeware algorithms

for calculating log P in log Koc modeling to determine which

one or ones are best suited for use in these models. The use of

these freeware algorithms aimed to obtain log P values in a

quick and easy way in order to facilitate the reproduction of

results. The models obtained were tested and validated ac-

cording to guidelines from the literature (Kiralj and Ferreira,

2009; Chirico and Gramatica, 2011; Roy et al., 2012) to ensure

that the models are reliable and useful for prediction.

training set.

Pesticide classes n Range of log Koc values

Acetonitriles 6 1.57e2.86

Carbamates 29 0.42e3.35

Dinitroanilines 8 2.92e4.01

Organochlorinates 8 3.00e5.31

Organophosphates 28 1.20e4.66

Phenylureas 44 1.29e3.55

Triazines 13 1.71e3.07

Di- and Triazoles 7 1.83e3.73

Total 143 0.42e5.31

2. Materials and methods

2.1. Experimental Koc data

The experimental values of the soil sorption coefficients

(log Koc) of 163 nonionic organic pesticides were obtained from

the literature (Sabljic et al., 1995; Gramatica et al., 2000). These

data were divided into a training set of 143 compounds and an

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

external validation set of 20 compounds (test set A). These

same sets were used in the studies by Gramatica et al. (2000),

Huuskonen (2003) and Duchowicz et al. (2007). The com-

pounds that comprised the training set are heterogeneous and

belong to different classes (Table 1), and the log Koc values

range from 0.42 to 5.31, whereas the test set A values range

from 0.56 to 4.50. A second external validation set (test set B),

which comprised data from 38 pesticides, was extracted from

Huuskonen (2003). The test set B log Koc values range from 0.30

to 6.00. Therefore, a training set of 143 pesticides was used for

the model design, and two external validation sets (A and B)

were used to evaluate the predictive capabilities. The experi-

mental log Koc values of all compounds used in the present

work are shown in Table S1 (SupplementaryMaterial). The use

of these datasets from previous studies facilitated the com-

parison of our results with those from the literature

(Gramatica et al., 2000; Huuskonen, 2003; Duchowicz et al.,

2007).

2.2. Assessment of log P and QSPR models

The following freeware algorithms were used to assess the

log P values of each compound: ALOGP, ALOGPS, ACLOGP,

MLOGP, KOWWIN, XLOGP2 and XLOGP3. Details about all al-

gorithms are presented in Table 2 (Mannhold and van de

Waterbeemd, 2001; Tetko et al., 2001; Cheng et al., 2007;

Todeschini and Consonni, 2009).

The log P values of each algorithm and the experimental

values were obtained by entering the simplified molecular

input line entry specification (SMILES) of each compound in

ALOGPS 2.1 software (http://www.vcclab.org/lab/alogps). The

log P values of all of the compounds from the training set and

test sets A and B are shown in Table S1 (Supplementary

Material).

Themodels for estimating log Koc as a function of log Pwere

developed by simple linear regression using Minitab 15.0

(Minitab Inc., USA) and QSAR Modeling (freeware developed

and donated by the Laboratory for Theoretical and Applied

Chemometrics at State University of Campinas, Brazil) soft-

ware. Thus, seven models could be designed for predicting

log Koc because seven algorithms were used to calculate log P.

Next, the statistical qualities of themodelswere assessed, and

the models were compared with one another.

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Table 2 e Algorithms used for log P predictions.

Algorithms Calculation approach Number of compounds in thecalibration dataset

ALOGP 115 atom contributions 8364

ALOGPS 75 E-state indices 12,908

ACLOGP 369 atom contributions 5000

MLOGP 13 MLR parameters 1230

KOWWIN 150 group contributions and 250 correction factors 2351

XLOGP2 90 atom contributions and 10 correction factors 1863

XLOGP3 87 atom contributions and 2 correction factors 8199

wa t e r r e s e a r c h x x x ( 2 0 1 3 ) 1e94

2.3. Statistical quality and predictive power

The goodness of fit is measured in terms of how well the

model specification fits the experimental data. The evaluation

is performed by calculating the coefficient of determination

(R2), the predicted residual error sum of squares of calibration

(PRESScal) and the standard error of calibration (SEC ). To

qualify as good-fit models, QSPR models should achieve

values of R2 > 0.6 and be as close as possible to 1, and the

PRESScal and SEC values should be as close as possible to zero

(Gaudio and Zandonade, 2001).

Model significance was evaluated using the statistical F-

test. The value of the F statistic in the regression should be

higher than a tabulated reference value at 5% significance

level (a ¼ 0.05). The higher the value is of the F statistic ob-

tained in the regression compared with the tabulated value,

the more significant the model.

Internal or leave-one-out (LOO) cross-validation is per-

formed as follows. Each object is excluded one-by-one from

the model. The model is reconstructed without the excluded

object, and the value of the excluded object is calculated.

Finally, the sum of squares of differences between predicted

and observed values (PRESSval), the cross-validation determi-

nation coefficient ðQ2LOOÞ and the standard error of validation

(SEV) are calculated. A model can be considered validated

when the value of SEV is close to zero, and Q2LOO is higher than

0.5 (Todeschini and Consonni, 2009).

The robustness test, or leave-N-out (LNO) cross-validation

evaluates whether the models are significantly affected by

small and deliberate changes in their parameters. In this test,

the model is recalculated upon the systematic removal of up

to N elements from the training set. Typically, a maximal N in

the range of 25e30% of the original set is used. In this study, N

ranged from 1 to 40, with six replicates for each value of N.

This method results in greater disturbance than the LOO

validation. The mean value of Q2LNO should be higher than 0.5

and as close as possible to the Q2LOO value for a model to be

considered robust (Kiralj and Ferreira, 2009).

The possibility of chance correlation can be tested by Y-

randomization. This test assesses whether the relationship

between the explanatory variables and the response variable

only resulted from randomness. The models are recalculated

after vector Y (response variable) randomization, and thus a

significant worsening of the statistical parameters of these

new regressions is expected. This procedure was repeated 50

times, and two graphs were plotted in which the parameters

R2 and Q2LOO (the y axis) were introduced as a function in the x-

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

axis of the values of a correlation coefficient R(Yo,Yr), derived

from the original and randomized Y values. The intercepts of

these graphs should be lower than 0.3 for R2 and 0.05 for Q2LOO

to rule out the possibility of chance correlation (Eriksson et al.,

2003).

The predictive power of a regressionmodel is measured by

external validation. The external validation of this study’s

models was conducted by predicting the log Koc values for the

test sets. The predictive power of regression models can be

assessed using the determination coefficient of external vali-

dation ðR2PREDÞ, and the modified determination coefficients of

external validation (r2m). The values of such coefficients

should be higher than 0.5 in both cases (Todeschini and

Consonni, 2009; Mitra et al., 2011). The values of the stan-

dard error of prediction (SEP), the predicted residual error sum

of squares of external validation (PRESSev), and the average

relative error (ARE ) were also assessed, which should be as

low as possible, in addition to the slopes of the regression

lines, which intercept the origin between the observed and

predicted values. The values of these slopes (k and k0) shouldfall between 0.85 and 1.15, and the absolute value of the dif-

ference between the R20 and R02

0 coefficients should be lower

than 0.3 (Golbraikh and Tropsha, 2002; Golbraikh et al., 2003;

Tropsha et al., 2003).

The analysis of the residual plots was performed to assess

whether the obtained residuals have normal distribution and

mean zero. The formulas used to calculate all of the statistical

parametersmentionedaboveareprovided in theSupplementary

Material.

After these validation procedures, the best models were

compared with other models previously reported in the liter-

ature (Gramatica et al., 2000; Huuskonen, 2003; Duchowicz

et al., 2007) that used the same sets of nonionic pesticides in

their development and validation.

3. Results and discussion

3.1. Correlation between experimental and calculatedlog P values

The log P experimental data are not always available for all of

the compounds used in a QSPR study. Therefore, the first step

of the present study was to evaluate the Pearson’s correlation

coefficient (r) of the log P values generated by each of the seven

tested algorithms and the respective experimental data. Such

data enabled a preliminary evaluation to determine which

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Table 3 e Pearson’s correlation coefficient of log P dataobtained using different prediction algorithms andexperimental data (n [ 137).

Log P algorithm r

ALOGP 0.933

ALOGPS 0.972

ACLOGP 0.921

MLOGP 0.815

KOWWIN 0.973

XLOGP2 0.892

XLOGP3 0.975

Table 5 e Statistical parameters of the log Koc predictionmodels.

Model R2 SEC PRESScal F Q2LOO SEV PRESSval

1 0.632 0.536 40.462 242.22 0.619 0.542 41.941

2 0.744 0.447 28.139 410.04 0.735 0.451 29.146

3 0.667 0.510 36.637 282.22 0.656 0.515 37.858

4 0.567 0.581 47.569 184.96 0.552 0.587 49.247

5 0.713 0.473 31.586 349.90 0.701 0.480 32.914

6 0.619 0.545 41.909 228.99 0.606 0.551 43.354

7 0.695 0.488 33.535 321.37 0.686 0.492 34.578

wat e r r e s e a r c h x x x ( 2 0 1 3 ) 1e9 5

algorithms would better fit the observed values and, conse-

quently, generate the best and most realistic QSPR models.

These results are provided in Table 3. The algorithms XLOGP3,

KOWWIN and ALOGPS demonstrated the best correlations

between the calculated and the experimental data.

3.2. Log Koc prediction models

The models for estimating log Koc, considering the different

algorithms for calculating log P, are shown in Table 4. The

parameters required to evaluate their statistical quality are

provided in Table 5.

The F-test results (Table 5) showed that all of the re-

gressions are statistically significant at 5% significance

(a ¼ 0.05) because the F parameter values are higher than the

tabulated F value (F1,141 ¼ 3.9) in all of the cases. Furthermore,

the most significant models at 5% were developed using the

algorithms ALOGPS, KOWWIN and XLOGP3 (models 2, 5 and 7,

respectively).

In addition, the statistical data indicated that the models

with the best fit were obtained using the algorithms ALOGPS,

KOWWIN and XLOGP3. These models exhibited the highest R2

and Q2LOO values and the lowest SEC and SEV values. Algorithm

choice clearly significantly affected the statistical quality of

the regression models. For example, model 4, which was ob-

tained using the algorithm MLOGP, displayed a statistical

quality far below the others and was rejected by the goodness

of fit criterion (R2 < 0.6).

The SEC values should be as low as possible and within the

limits imposed by the experimental error of the measured

response variables. Lohninger (1994) showed that there are

limits to the accuracy of experimental measurements of

log Koc. In a study performed on a set of pesticides with at least

Table 4 e Models for predicting the log Koc of nonionicpesticides (n [ 143).

Model Equation

1 log Koc ¼ 1.030 þ 0.576 ALOGP

2 log Koc ¼ 0.945 þ 0.560 ALOGPS

3 log Koc ¼ 0.999 þ 0.548 ACLOGP

4 log Koc ¼ 1.200 þ 0.574 MLOGP

5 log Koc ¼ 1.100 þ 0.497 KOWWIN

6 log Koc ¼ 1.300 þ 0.493 XLOGP2

7 log Koc ¼ 1.000 þ 0.536 XLOGP3

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

4 different log Koc values for each compound, the average

standard deviation of these measurements was 0.44 log units.

Themodeling of a dependent variable is limited by the quality

of the available experimental data. Thus, all of themodels that

we constructed have SEC values that are compatible with the

experimental error.

All of the models were approved in the LOO cross-

validation because they exhibited a Q2LOO higher than 0.5.

Therefore, according to the data provided in Table 5, regarding

the fit of the data and the LOO cross-validation, the models

may be placed in descending order of statistical quality as a

function of the algorithm used as follows:

ALOGPS > KOWWIN > XLOGP3 > ACLOGP > ALOGP

> XLOGP2 > MLOGP

LNO cross-validation and the chance correlation test are

critical tools for assessing the internal quality of the models.

LNO cross-validation data showed that all of the models had

mean Q2LNO values close to their respective Q2

LOO value (Table 6).

The cross-validation graph for model 2 (ALOGPS) (Fig. 1)

demonstrates that the model is stable and resistant to small

variations. The graphs of the remaining models are available

in the Supplementary Material and invite the same conclu-

sion. However, although all of the models could be considered

robust, it again became clear that their statistical quality was

affected by the choice of log P algorithm because the mean

Q2LNO values differed between models, as previously observed

for the R2 and Q2LOO values.

Chance correlation was evaluated using the Y-randomi-

zation test. The results (Table 6) indicated the absence of

chance correlation in all of the models designed, considering

Table 6 e LNO cross-validation and chance correlationtest data.

Models Robustness Chance correlation

MeanQ2

LNO

Intercept R2

vs. R(Yo,Yr)Intercept Q2

LOO

vs. R(Yo,Yr)

1 0.618 �0.033 �0.061

2 0.734 �0.033 �0.062

3 0.655 �0.043 �0.074

4 0.551 �0.030 �0.059

5 0.700 �0.034 �0.064

6 0.604 �0.029 �0.059

7 0.685 �0.036 �0.065

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Fig. 1 e Graph of the LNO cross-validation of model 2

(ALOGPS).

wa t e r r e s e a r c h x x x ( 2 0 1 3 ) 1e96

that the values of the intercepts of the R2 and Q2LOO graphs as a

function of R(Yo,Yr) are within the range (lower than 0.3 and

0.05, respectively) proposed by Eriksson et al. (2003).

The external validation results for test set A (Table 7)

clearly demonstrated that the predictive power of the models

was significantly affected by the choice of log P algorithm. The

best models were those designed using the KOWWIN and

ALOGPS algorithms. Such models showed the highest R2PRED

values and the lowest SEP, PRESSev and ARE values. ARE is a

statistical parameter that indicates the mean percentage of

variation between the predicted and observed values. That is,

ARE is a relative measure, whereas PRESSev is an absolute

measure of residuals (the difference between the predicted

and the observed values). Thus, the ARE values can be

considered critical to the choice of the best model for models

with similar PRESSev values.

The model constructed using the MLOGP algorithm was

rejected by external validation (R2PRED < 0.5). The predictive

power of the models in relation to the algorithm used can be

expressed in descending order:

KOWWIN > ALOGPS > ACLOGP > XLOGP3 > XLOGP2

> ALOGP > MLOGP

Regarding the k, k0 and��R2

0 � R020

�� parameters, all of the

models are within the range proposed by Tropsha et al. (2003).

However, upon analysis of themodified correlation coefficient

(r2m) values, the models generated using the ALOGP, MLOGP

and XLOGP3 algorithms were rejected because they exhibited

r2m values lower than 0.5 in both regression graphs (observed

versus predicted values and predicted versus observed

values).

Table 7 e Statistical data of the first external validation (test se

Model R2PRED SEP PRESSev ARE (%)

1 0.522 0.644 8.29 28.6 0

2 0.668 0.536 5.75 16.1 0

3 0.638 0.560 6.27 24.0 0

4 0.437 0.698 9.75 28.1 0

5 0.733 0.481 4.62 18.0 0

6 0.550 0.624 7.80 26.4 0

7 0.563 0.615 7.57 22.3 0

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

To assist in the selection of the best models, the results

were also analyzed considering the stricter criteria proposed

by Chirico and Gramatica (2011): R2 higher than 0.7 and Q2LOO

and R2PRED higher than 0.6. Thus, only models obtained using

the ALOGPS and KOWWIN algorithms can be considered to

display a good fit and an effective predictive capability.

Therefore, out of the seven initial models, only two meet the

statistical criteria regarding the data fit, internal validation

and predictive power. Regarding the analysis of the residuals,

the graphs provided in the Supplementary Material demon-

strate that the residuals may be regarded as having normal

distribution and mean zero.

Tests were conductedwith a second external validation set

(test set B). The results are provided in Table 8 for the two best

models (2 and 5). Both models presented similar results.

However, model 2 (ALOGPS) was slightly superior to model 5

(KOWWIN) regarding ARE. The graphs of the observed values

as a function of the predicted values of log Koc for thesemodels

are shown in Figs. 2 and 3.

The concordance between the predicted values of each

model was reasonable. For 34 of the 38 compounds tested, the

differences between the predicted values in model 2 and the

predicted values in model 5 were less than 0.5. Oxydemeton-

methyl presented the greatest difference between the pre-

dicted values (0.81), but this difference was compatible with

the SEC values ofmodels 2 and 5 (0.447 and 0.473 respectively).

In other hands, amitrole and fluvalinate were outliers in

bothmodels (differences between predicted and experimental

values >2.5 � SEC ). The log Koc experimental values in the

training set were in the range 0.42e5.31 (Table 1). Fluvalinate

(experimental log Koc ¼ 6) is outside in this range. For amitrole,

Huuskonen (2003) reported there might be experimental error

in value used.

Considering the results obtained, it was evident that using

a particular algorithm to calculate log P without a prior se-

lection or comparison could lead to poor results in a QSPR

study. Therefore, it was observed that different algorithms

could contribute different types and quantities of log P-enco-

ded information, thus leading to models with significant dif-

ferences in their statistical qualities and predictive power

(Melo and Ferreira, 2009).

The soil sorption of nonionic compounds occurs through a

mechanism involving hydrophobic interactions because the

primary domain of this sorption is soil organic matter. Thus,

van der Waals forces are the main forces acting on the in-

teractions between sorbedmolecules and the sorbent surface.

Therefore, the hydrophobicity of amolecule is fundamental in

t A, n [ 20).

r2m r02m��Dr2m

�� k k0

��R2

0 � R020

��

.491 0.406 0.085 0.93 1.01 0.067

.558 0.685 0.127 0.97 0.99 0.050

.588 0.523 0.065 0.95 1.00 0.033

.390 0.279 0.111 1.04 0.90 0.139

.663 0.632 0.031 1.00 0.97 0.011

.527 0.495 0.032 0.92 1.03 0.020

.475 0.498 0.023 0.99 0.95 0.016

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Table 8e Statistical data of the second external validation(test set B, n [ 38).

Model R2PRED SEP PRESSev ARE (%)

Model 2 e ALOGPS 0.768 0.621 14.66 20.5

Model 5 e KOWWIN 0.769 0.620 14.62 23.8

Fig. 3 e Graph of observed values as a function of predicted

values of log Koc for validation set B of model 5.

wat e r r e s e a r c h x x x ( 2 0 1 3 ) 1e9 7

this process and can be measured by log P. The correct mea-

surement of the value of log P is essential in the determination

of a soil sorption coefficient of a given chemical compound.

In QSPR studies in which the hydrophobic parameter

significantly affects the mechanism of the investigated phe-

nomenon, the experimental values of log P, when available,

should always be the first choice for the descriptor. Thus, a

prediction model should only be employed when the experi-

mental values are impossible to use because experimental

values are more accurate and reliable than predicted values.

In the present study, log Koc modeling was performed using

estimated log P data. Algorithms that are able to generate the

best estimates of log P with respect to its true value (experi-

mental value) are the most suitable. The predictive power of

algorithms is affected by the amount and structural diversity

of compounds used in their calibrations. In this way, the

chosen algorithmmust be calibrated in such away tomeet the

chemical space of relevant substances. An algorithm that has

been calibrated for a particular class of compounds will not be

suitable for the estimation of log P values for structurally

different compounds.

Thus, in the analysis of data related to the characteristics

of each algorithm (Table 2), ALOGPS was calibrated from a

dataset with 12,908 compounds (the largest calibration data-

set used). Therefore, due to the extent of its chemical space,

this algorithm has a good chance of producing acceptable

predictions of log P. Namely, as the calibration datasets im-

proves, the chemical spaces serviced by the algorithm

increase.

The log P calculation approach is also an important feature

for consideration. Many previous studies (Mannhold and van

de Waterbeemd, 2001) have demonstrated that algorithms

that sum group/fragment contributions produce better results

than those that add atom-type contributions. In addition,

additive methods that contain correction factors are more

accurate thanmethods that do not use these factors. Thus, the

Fig. 2 e Graph of observed values as a function of predicted

values of log Koc for validation set B of model 2.

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

number of classified contributions of fragments or atoms-

types and the number of correction factors can affect the

predictions. Accordingly, among these algorithms (Table 2),

KOWWIN is more suitable than the other algorithms. There-

fore, the choice of algorithm for log P predictions cannot be

arbitrary but must be based on the chemical structure of the

compounds of interest and the characteristics of the available

algorithms.

The best Pearson’s correlation (Table 3) between experi-

mental and calculated log P was obtained using the XLOGP3

algorithm. However, this outcome did not ensure that this

algorithm was the most appropriate for modeling log Koc.

Therefore, all of the results should be considered when

choosing the best algorithm, and the most appropriate algo-

rithm should be selected using thoroughly tested and vali-

dated models. Furthermore, as observed in the present study,

more than one algorithm can be considered adequate to

developing statistically reasonable models.

3.3. Comparison with QSPR models from the literature

Models 2 and 5were comparedwith othermodels found in the

literature that were obtained using the same dataset. The

main statistical parameters of these models and their

respective external predictive power (sets A and B) are sum-

marized in Table 9. Although their R2 and Q2LOO were lower

than in the literature models, models 2 and 5 exhibit a pre-

dictive power similar to that of the remainder.

In addition, the SEC values of models 2 and 5 were higher

than those of the models from the literature. However, the

development of prediction models with errors that are much

lower than the experimental error is not appropriate. Thus,

models 2 and 5 may be considered reliable for estimating

log Koc because they present SEC values that are consistent

with the experimental error (0.44) (Lohninger, 1994).

The goodness of fit of a regression model can be improved

by increasing the number of explanatory variables. However,

the inclusion of an excessive number of these variables can

cause overfitting (an artificial increase in R2 values) of the

model. In QSPR studies, one explanatory variable is conven-

tionally included per each group of five compounds of the

dataset (Gaudio and Zandonade, 2001). The models from the

literature have 6 or 12 explanatory variables. That is, they are

different log P algorithms on themodeling of the soil sorptionx.doi.org/10.1016/j.watres.2013.06.053

Table 9 e Comparison of log Koc models.

Model No. of variables R2 SEC Q2LOO R2

PRED(A) ARE (%) R2PRED(B) ARE (%)

Model 2 e ALOGPS 1 0.744 0.447 0.735 0.668 16.1 0.768 20.5

Model 5 e KOWWIN 1 0.713 0.473 0.701 0.733 18.0 0.769 23.8

Gramatica et al. (2000) 6 0.843 0.35 0.824 0.67 27.4a NA NA

Huuskonen (2003) 12 0.82 0.37 0.79 0.79 20.0a 0.74 29.6a

Duchowicz et al. (2007) 6 0.90 0.29 0.89 0.71 21.7a NA NA

NA ¼ not available in the source article.a Calculated by the authors of the present study using experimental and predicted values available in the literature.

wa t e r r e s e a r c h x x x ( 2 0 1 3 ) 1e98

within the predefined range. The univariate models 2 and 5,

although with lower R2 and Q2LOO values, display better ARE

values. The use of a large number of descriptors can lead to a

false impression that the model is well fitted. Thus, the better

goodness of fit of the models from the literature can be simply

explained by the use of a larger number of explanatory vari-

ables. However, the higher goodness of fit did not translate

into the respective predictive powers of these models. The

models had values similar to those obtained by univariate

models 2 and 5.

Another positive aspect of models 2 and 5 compared with

the others is that the models from the literature were devel-

oped using molecular descriptors that required chemically

more complex mechanistic interpretation than the log P

parameter. Thus, the appropriate choice of log P algorithm

enabled the design of mathematically simpler models with

straightforward physicochemical interpretation and with

predictive powers similar to those ofmore complexmodels. In

such situations, the principle of Occam’s razor is applicable:

when there are several possible solutions for the same prob-

lem, the simplest explanation should be adopted (Gaudio and

Zandonade, 2001). Therefore, the models proposed in the

present study fulfill this simplicity requirement and are reli-

able in relation to their statistical quality and predictive

power.

4. Conclusions

The results of this study clearly demonstrated that when the

experimental log P values are unavailable, the choice of the

algorithm to calculate such values based on the molecular

structure could affect the final result of a QSPR study. There-

fore, this choice cannot be arbitrary because it could result in

the design of models that are inadequate to predict the

property under study. Thus, many of the models reported in

the literature that did not consider algorithm choice might be

improved if the best-suited log P algorithm is determined.

In this study, the best QSPR models for predicting the soil

sorption coefficient of nonionic pesticides were obtained

using the algorithms ALOGPS and KOWWIN. However, these

two methods should not be generalized to any QSPR study.

This study demonstrated that only the algorithms selected

herein were the best-suited algorithms for the log Koc

modeling of this dataset. Thus, the choice of the best log P

algorithm for a QSPRmodel should bemade on a case-by-case

basis, considering the propertymodeled, the training set used,

and the characteristics of available algorithms.

Please cite this article in press as: dos Reis, R.R., et al., The effect ofcoefficient of nonionic pesticides, Water Research (2013), http://d

Acknowledgments

The authors thank the MCT/CNPq/Fundacao Araucaria (www.

fundacaoaraucaria.org.br) and the National Counsel of Tech-

nological and Scientific Development (CNPq, www.cnpq.br)

for providing the financial support for this study.

In addition, the authors thank the Laboratory for Theo-

retical and Applied Chemometrics at State University of

Campinas (LQTA/UNICAMP) for providing the QSAR Modeling

software.

Appendix A. Supplementary material

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.watres.2013.06.053.

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