*Chem. Res. Toxicol.* **2002,** *15,* 1229-1234

**Influence of the Structural Diversity of Data Sets on the\
Statistical Quality of Three-Dimensional Quantitative\
Structure-Activity Relationship (3D-QSAR) Models:\
Predicting the Estrogenic Activity of Xenoestrogens**

Seong Jae Yu,^†^ Susan M. Keenan,^†^ Weida Tong,^‡^ and William J.
Welsh\*^,†^

*Department of Pharmacology, University of Medicine & Dentistry of New
Jersey,\
Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New
Jersey 08854, and\
National Center for Toxicological Research, 3900 NCTR Road, Jefferson,
Arkansas 72079*

*Received July 30, 2002*

Federal legislation has resulted in the two-tiered in vitro and in vivo
screening of some 80 000 structurally diverse chemicals for possible
endocrine disrupting effects. To maximize efficiency and minimize
expense, prioritization of these chemicals with respect to their
estrogenic disrupting potential prior to this time-consuming and
labor-intensive screening process is essential. Computer-based
quantitative structure-activity relationship (QSAR) models, such as
those obtained using comparative molecular field analysis (CoMFA), have
been demonstrated as useful for risk assessment in this application. In
general, however, CoMFA models to predict estrogenicity have been
developed from data sets with limited structural diversity. In this
study, we constructed CoMFA models based on biological data for a
structurally diverse set of compounds spanning eight chemical families.
We also compared two standard alignment schemes employed in CoMFA,
namely, *atom -fit* and *flexible field-fit,* with respect to the
predictive capabilities of their respective models for structurally
diverse data sets. The present analysis indicates that *flexible
field-fit* alignment fares better than *atom -fit* alignment as the
structural diversity of the data set increases. Values of log(RP), where
RP = relative potency, predicted by the final *flexible field -fit*
CoMFA models are in good agreement with the corresponding experimental
values. These models should be effective for predicting the endocrine
disrupting potential of existing chemicals as well as prospective and
newly prepared chemicals before they enter the environment.

**Introduction**

Certain man-made and naturally occurring chemicals have been shown to
bind to the estrogen receptor (ER)^1^ *(1).* As a member of the
superfamily of nuclear hormone receptors, the ER functions as a DNA
transcription factor. Activation of estrogen response elements (EREs) by
estrogen receptor homodimers leads to the synthesis of new gene products
important for most notably sexual reproduction and differentiation
*(2).* Unregulated ER activation by exogenous chemicals has been shown
to disrupt the delicate endocrine balance of humans and of other species
as divergent as reptiles and fish *(3).* Many of the tens of thousand of
chemicals in use today require screening for possible endocrine
disrupting properties. This number will surely continue to increase by
virtue of modern technologies such as combinatorial chemistry *(4)* and
high throughput screening *(5)* associated with industrial
drug-discovery programs *(6, 7).*

\* To whom correspondence should be addressed. [[E-mail:
welshwj@]{.underline}](mailto:welshwj@)
[[umdnj.edu]{.underline}](http://umdnj.edu/).

> † Robert Wood Johnson Medical School.
>
> ‡ National Center for Toxicological Research.

1 Abbreviations: CoMFA, comparative molecular field analysis; DHT,
dihydrotestosterone; E2, 17*â*-estradiol; EDC, endocrine disrupt­ing
chemical; ER, estrogen receptor; ERE, estrogen response element; PC,
principal component; PDB, Protein DataBank; PXR, pregnane xenobiotic
receptor; QSAR, quantitative structure-activity relation­ship; RP,
relative potency; SXR, steroid and xenobiotic receptor.

The classic and still standard methods for measuring estrogenicity are
based on time-consuming and labor­intensive in vitro and/or in vivo
assays which are not suitable for routine screening of large numbers of
chemi­cals. Without a more expeditious in vitro protocol for the analysis
of endocrine disruption, methods must be de­veloped to predict the
endocrine disrupting potential of chemicals and thus enable scientists
to establish a priority list to direct the further testing of potential
endocrine disrupting compounds (EDCs). Molecular mod­eling has emerged as
a valuable tool for the prediction of biological activities. In
particular, use of quantitative structure-activity relationship (QSAR)
models to predict the endocrine disrupting properties of compounds will
yield significant savings in time and expense and ulti­mately provide
improved environmental protection *(8, 9).*

Estrogenic EDCs are structurally diverse and span a wide range of
chemical families; therefore, QSAR models need to be equally inclusive
in terms of structural diversity. Previous CoMFA models for predicting
estro­genicity have been constructed largely from data sets with limited
structural diversity *(9).* One exception is the CoMFA model developed
by Waller et al. for a series of structurally diverse estrogenic
compounds *(10)* using the SEAL ([S]{.underline}teric and
[E]{.underline}lectrostatic [Al]{.underline}ignment) *(11)* alignment
scheme. In this study, we present CoMFA models based on a structurally
diverse data set that spans eight

10.1021/tx0255875 CCC: \$22.00 © 2002 American Chemical Society\
Published on Web 10/01/2002

**Figure 1.** Structures and basic alignment scheme of chemicals in the
training set. The first number in column 1 refers to the total number of
representatives of that particular chemical class in the data set, while
the number in parentheses refers to those representatives that exhibited
measurable biological activity and, thus, were used for model
development.![](media/image1.png){width="1.6333333333333333in"
height="0.9395833333333333in"}![](media/image2.png){width="0.7604166666666666in"
height="3.876388888888889in"}![](media/image3.png){width="1.2763888888888888in"
height="0.22013888888888888in"}![](media/image4.png){width="1.3166666666666667in"
height="0.6472222222222223in"}![](media/image5.png){width="1.5034722222222223in"
height="1.0833333333333333in"}

chemical families (Figure 1). In addition, we explore the influence of
the size and structural diversity of the data set on the statistical
quality and predictive capability of the resulting 3D-QSAR models, using
the two most frequently employed alignment schemes of *atom-fit* and
*flexible field-fit.* The present results for estrogenic EDCs indicate
that *flexible field-fit* exhibits improved perfor­mance over *atom-fit*
as the size and structural diversity of the data set increase. The
advantage of field-based over atom-based alignment schemes is a logical
consequence of the known promiscuity of ER α and *β.* Other receptors
that share this propensity for binding ligands from disparate chemical
families, such as the human steroid and xenobiotic receptor (SXR) *(12,
13)* found in humans and its pregnane xenobiotic receptor (PXR)
orthologue found in other mammalian species *(14),* would be ex­pected to
follow a similar pattern in terms of their corresponding 3D-QSAR models.
Such models that can maintain their statistical quality and predictive
ability even for structurally diverse data sets are highly desir­able in
these cases. This is particularly critical in risk assessment scenarios
where QSAR and 3D-QSAR models are expected to predict the endocrine
disrupting effects of chemicals with virtually zero tolerance for false
nega­tives.

**Experimental Procedures**

**Data Sets for Analysis.** The data set consisted of 53 compounds from
8 structurally diverse chemical families (Figure 1). Values of the
relative potency (RP) of the chemicals in the data set were obtained
from a yeast-based reporter gene assay. The recombinant yeast cell
bioassay (RCBA) utilized in this study is a highly sensitive human
estrogen receptor-based assay

**Table 1. Chemical Classes Represented in the\
Respective Training Sets for CoMFA Models 1-5**

![](media/image6.jpeg){width="3.2840277777777778in"
height="1.1854166666666666in"}

the likes of which have been used previously for the detection of
xenoestrogens *(15)* and in various estrogen receptor studies *(16).*
While the RCBA yields potency values (relative to E2) similar to
literature values, the assay exhibits greater sensitivity than other in
vitro and in vivo assays. By virtue of its increased sensitivity, the
assay was able to measure detectable values of partial agonist activity
of the ER antagonists tamoxifen and 4-hydroxytamoxifen. Consequently, we
decided to include both tamoxifen and 4-hydroxytamoxifen in the data set
for model building. Another justification for inclusion of these two
com­pounds is due to the fact that the RCBAs are based on the expression
of the human ER; thus, the present 3D-QSAR models take into account the
potential estrogenic effects these com­pounds have on humans. RP was
defined as 100 times the ratio of the concentration of 17*β*-estradiol
(E~2~) giving 50% induction in *β*-galactosidase activity (EC~50~) and
the EC~50~ of the tested compounds. Using this scheme, the RP of E~2~
equals 100 *(17).* As is standard in QSAR studies, we excluded eight
compounds from the data set as these compounds showed undetectable
estrogenic activity under the assay conditions. This pruned data set
consisted of 10 steroids, 5 synthetic estrogens, 2 antiestro­gens, 5
lactones, 6 phytoestrogens, 3 alkylphenols, 11 orga­nochlorines, and 2
"other" chemicals not belonging to any of these classes. We divided the
data set into a training set containing 40 compounds (Table 2) used for
model development, and a test set of 4 randomly selected compounds
(Table 4) used for model validation.

**Molecular Modeling.** All molecular modeling and statistical analyses
were performed on a Silicon Graphics O2 workstation running under the
IRIX 6.5 operating system using Sybyl 6.7 (Tripos, St. Louis, MO)
*(18).* The crystal structures of the ER-bound ligands E~2~ (1A52)
*(19)* and diethylstilbestrol (3ERD) *(20),* extracted from the Protein
DataBank (PDB), were used as structural templates to build the steroids
and synthetic estro­gens. Molecular structures for the remaining
compounds were constructed from the Sybyl 6.7 fragment database *(18),*
after which they were energy-minimized to the putative global low­energy
conformation. Using the standard Tripos molecular force field with a
distance-dependent (1/*r*) dielectric function, mol­ecules were first
geometry-optimized to the nearest local minimum-energy conformation
until an energy difference of 0.001 kcal/mol between successive
iterations was achieved. All rotatable (single) bonds were then
systematically searched in 10° increments, and, after setting each
torsion angle to its minimum-energy conformation, the molecule was
energy mini­mized a final time. Atomic partial charges were computed
using the Gasteiger-Huckel method *(21).*

**CoMFA Alignment.** The initial and arguably the most important step in
CoMFA model development is the alignment of molecules to a template. The
compounds must be aligned to ensure maximal superimposition of their
steric and electrostatic fields. As our data set is structurally diverse
and contains compounds belonging to several different chemical families,
there are many possible alignment schemes. In this study, we compared
two different alignment schemes, namely, *atom -fit* and *flexible field
-fit,* in terms of the statistical quality and predictive ability of
their respective CoMFA models. Both alignment schemes used E~2~ as the
template molecule. Using *atom -fit,* which aligns a set of molecules by
a rigid least-squares fit of preselected common atoms in each molecule,
and *flexible field-fit,* which aligns a set of molecules by seeking
each

**Table 2. Comparison of Experimentally Observed and\
CoMFA-Predicted Activities \[log(RP)\] Using Model 5,\
Based on *Atom -Fit* and *Flexible Field -Fit ***Alignment\
Schemes for the 40 Training Set Compounds

+------------------------+-------+---------+--------+--------+------+
|                        | >     |         |        |        |      |
|                        | CoMFA |         |        |        |      |
|                        | -pred |         |        |        |      |
|                        | icted |         |        |        |      |
|                        | > (   |         |        |        |      |
|                        | model |         |        |        |      |
|                        | > 5)  |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| chemical name          | exptl |         |        |        |      |
|                        | *atom |         |        |        |      |
|                        | -fit* |         |        |        |      |
|                        | res   |         |        |        |      |
|                        | idual |         |        |        |      |
|                        | *     |         |        |        |      |
|                        | field |         |        |        |      |
|                        | -fit* |         |        |        |      |
|                        | res   |         |        |        |      |
|                        | idual |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| 17*β*-estradiol        | 2.00  | 0.79    | 1.21   | 0.91   | 1.09 |
+------------------------+-------+---------+--------+--------+------+
| 17*β* -estradiol‑      | -0.50 | -0.60   | 0.10   | -0.84  | 0.34 |
+------------------------+-------+---------+--------+--------+------+
| > 3(*β*-D-glucuronide) | -2.00 | -1.99   | -0.01  | -2.07  | 0.07 |
|                        |       |         |        |        |      |
| 17*β* -estradiol-      |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| > 3-sulfate            | 0.72  | 0.86    | -0.14  | 0.77   | -    |
|                        |       |         |        |        | 0.05 |
| 17α-estradiol          |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| estriol                | -0.20 | 0.88    | -1.08  | 0.62   | -    |
|                        |       |         |        |        | 0.82 |
+------------------------+-------+---------+--------+--------+------+
| testosterone           | -3.00 | -2.06   | -0.94  | -2.51  | -    |
|                        |       |         |        |        | 0.49 |
+------------------------+-------+---------+--------+--------+------+
| androstenediol         | -1.64 | -2.10   | 0.46   | -2.34  | 0.70 |
+------------------------+-------+---------+--------+--------+------+
| dehydroepi‑            | -2.74 | -2.76   | 0.02   | -2.41  | -    |
|                        |       |         |        |        | 0.33 |
+------------------------+-------+---------+--------+--------+------+
| > androsterone         | -3.40 | -2.98   | -0.42  | -3.46  | 0.06 |
|                        |       |         |        |        |      |
| D-norgestrel           |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| 17 α-ethylnylstra diol | 1.95  | 1.18    | 0.77   | 1.64   | 0.31 |
+------------------------+-------+---------+--------+--------+------+
| mestranol              | 0.86  | 1.34    | -0.48  | 1.63   | -    |
|                        |       |         |        |        | 0.77 |
+------------------------+-------+---------+--------+--------+------+
| diethylstilbestrol     | 1.87  | 2.3 7   | -0.50  | 1.85   | 0.02 |
+------------------------+-------+---------+--------+--------+------+
| hexestrol              | 1.49  | 1.78    | -0.29  | 1.37   | 0.12 |
+------------------------+-------+---------+--------+--------+------+
| dienestrol             | 1.40  | 1.45    | -0.04  | 1.26   | 0.15 |
+------------------------+-------+---------+--------+--------+------+
| tamoxifen              | -2.3  | -2.10   | -0.23  | -2.44  | 0.11 |
|                        | 3     |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| 4-hydroxytamoxifen     | -2.14 | -1.83   | -0.31  | -2.47  | 0.33 |
+------------------------+-------+---------+--------+--------+------+
| α -zearalenol          | 0.94  | 0.69    | 0.25   | 1.03   | -    |
|                        |       |         |        |        | 0.09 |
+------------------------+-------+---------+--------+--------+------+
| *β* -zearalenol        | -1.18 | -0.28   | -0.90  | -1.18  | -    |
|                        |       |         |        |        | 0.00 |
+------------------------+-------+---------+--------+--------+------+
| α -zearalanol          | 0.11  | 0.28    | -0.17  | 0.08   | 0.03 |
| (zeranol)              |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| *β* -zearalanol        | -0.34 | -0.62   | 0.28   | -0.29  | -    |
|                        |       |         |        |        | 0.05 |
+------------------------+-------+---------+--------+--------+------+
| coumestrol             | -0.17 | -0.3 5  | 0.18   | -0.04  | -    |
|                        |       |         |        |        | 0.13 |
+------------------------+-------+---------+--------+--------+------+
| equol                  | -1.07 | -1.42   | 0.3 5  | -0.89  | -    |
|                        |       |         |        |        | 0.18 |
+------------------------+-------+---------+--------+--------+------+
| daidzein               | -2.89 | -2.32   | -0.57  | -2.41  | -    |
|                        |       |         |        |        | 0.48 |
+------------------------+-------+---------+--------+--------+------+
| formononetin           | -2.25 | -2.08   | -0.17  | -2.23  | -    |
|                        |       |         |        |        | 0.03 |
+------------------------+-------+---------+--------+--------+------+
| genistein              | -1.31 | -1.97   | 0.66   | -1.50  | 0.19 |
+------------------------+-------+---------+--------+--------+------+
| 4-nonylphenol          | -2.66 | -2.59   | -0.07  | -3.02  | 0.36 |
+------------------------+-------+---------+--------+--------+------+
| 4-octylphenol          | -2.52 | -2.52   | -0.00  | -2.77  | 0.25 |
+------------------------+-------+---------+--------+--------+------+
| 4-*tert*-octylphenol   | -3.44 | -2.43   | -1.01  | -2.93  | -    |
|                        |       |         |        |        | 0.51 |
+------------------------+-------+---------+--------+--------+------+
| DDT                    | -4.52 | -4.3 8  | -0.14  | -4.27  | -    |
|                        |       |         |        |        | 0.25 |
+------------------------+-------+---------+--------+--------+------+
| *o*,*p\'*-DDT          | -3.96 | -4.3 0  | 0.34   | -4.17  | 0.21 |
+------------------------+-------+---------+--------+--------+------+
| *o*,*p\'*-DDE          | -4.40 | -3.90   | -0.50  | -4.03  | -    |
|                        |       |         |        |        | 0.37 |
+------------------------+-------+---------+--------+--------+------+
| 2,3,7,8-tetrachloro‑   | -0.59 | -0.49   | -0.10  | -0.53  | -    |
|                        |       |         |        |        | 0.05 |
+------------------------+-------+---------+--------+--------+------+
| > dibenzo -*p*-dioxin  | -1.22 | -1.98   | 0.76   | -1.53  | 0.31 |
|                        |       |         |        |        |      |
| 4                      |       |         |        |        |      |
| \'-chloro-4-biphenylol |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| 2                      | -2.43 | -2.50   | 0.07   | -2.26  | -    |
| \'-chloro-4-biphenylol |       |         |        |        | 0.17 |
+------------------------+-------+---------+--------+--------+------+
| 2\',5\'-dichloro-4-    | -0.21 | -0.78   | 0.57   | -0.34  | 0.13 |
+------------------------+-------+---------+--------+--------+------+
| > biphenylol           | 0.00  | -0.85   | 0.85   | 0.05   | -    |
|                        |       |         |        |        | 0.05 |
| 2\                     |       |         |        |        |      |
| ',4\',6\'-trichloro-4- |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| > biphenylol           | -0.09 | -0.79   | 0.70   | 0.11   | -    |
|                        |       |         |        |        | 0.19 |
| 2\',3\',4\',5\'-tetra- |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| > chloro-4-biphenylol  | -1.80 | -1.51   | -0.29  | -1.52  | -    |
|                        |       |         |        |        | 0.28 |
| 3,                     |       |         |        |        |      |
| 3\',5,5\'-tetrachloro- |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| > 4,4\'-biphenyldiol   | -2.3  | -2.82   | 0.52   | -2.72  | 0.42 |
|                        | 0     |         |        |        |      |
| bisphenol A            |       |         |        |        |      |
+------------------------+-------+---------+--------+--------+------+
| butylbenzylphthalate   | -3.40 | -3.66   | 0.26   | -3.50  | 0.10 |
+------------------------+-------+---------+--------+--------+------+

molecule's conformation that most closely resembles the steric and
electrostatic fields of the template molecule, we aligned the
training-set compounds with respect to the 3-hydroxylphenyl group of
E~2~. The *flexible field -fit* procedure sometimes distorts the
original molecular structure, and, therefore, each compound was
reminimized after field-fitting. The alignment schemes based on *atom
-fit* and *flexible field-fit* are illustrated in Figure 2A and Figure
2B, respectively.

**Calculation of CoMFA Descriptors.** The calculation of CoMFA steric
and electrostatic descriptors has been described in detail previously
*(8, 9).* Briefly, following alignment, the molecules were placed in a
three-dimensional cubic lattice with 2 Å spacing. Steric (van der Waals)
and electrostatic (Coulombic) field descriptors were calculated for each
molecule at all lattice points using a probe represented by an
sp^3^-hybridized carbon atom with a +1.0 charge. The steric and
electrostatic energy values were truncated to 30 and [+]{.underline}30
kcal/mol, respectively. The CoMFA field descriptors were scaled using
the CoMFA standard scaling method provided in Sybyl 6.7.

**Statistical Regression Methods.** The biological activity for the
40-compound training set was correlated with the CoMFA-­

generated steric and electrostatic fields using the statistical method
of partial least-squares (PLS) regression *(22).* Using PLS, the large
number of steric-electrostatic descriptors was reduced to a few
principal components (PCs) that are linear combinations of the original
descriptors. The optimum number of PCs was determined by the
Leave-One-Out (LOO) cross­-validation procedure *(23).* In this method,
each compound is systematically excluded once from the training set,
after which its activity is predicted by a model derived from the
remaining compounds. Using the optimal number of PCs, the final PLS
analysis was carried out without cross-validation to generate a
predictive QSAR model with a conventional correlation coef­ficient
*(23).*

**Results**

A data set containing 53 structurally diverse com­pounds was chosen for
the present study with the goal of analyzing the effectiveness of *atom
-fit* and *flexible field­-fit* alignment schemes for model generation as
the size and structural diversity of the data set increase. Unlike most
other CoMFA models associated with ER ligands that are based on
ligand-receptor binding data, the present models were constructed from a
functional assay whose high sensitivity was such that RP values
associ­ated with the partial agonist activity of the prototypical
antagonists tamoxifen and 4-hydroxytamoxifen were included for model
building. Values of the relative potency (RP) for the data set of
compounds were con­verted to log(RP) values for the construction of the
3D­QSAR models. After eliminating compounds which had no estrogenic
activity under assay conditions, the data set was divided into a
training set of 40 compounds (Table 2) and a test set of 4 randomly
selected compounds (Table 4) for model validation. We applied two
separate align­ment rules for CoMFA model generation. First, we aligned
the training set using an *atom-fit* alignment scheme to the
3-hydroxylphenyl group ofE2 (Figure 2A). Second, we applied a *flexible
field-fit* alignment scheme by aligning the 3D steric and electrostatic
field energies to the E2 template (Figure 2B).

CoMFA model 1 was developed using only the nine steroid compounds.
Although this particular data set is admittedly sparse as compared to
typical QSAR applica­tions, it is justified in the present case as a
benchmark for assessing the impact of gradually expanding the size and
structural diversity of the data set. As all steroid molecules embody a
four-ring system (see Figure 1) which is amenable to superimposition
using either alignment scheme, it is not surprising that the models
developed from *atom-fit* and *flexible field-fit* alignment schemes
were almost equally satisfactory insofar as their calcu­lated statistical
parameters (*r*~cv~^2^ = 0.576 vs 0.548; *r*^2^ *=* 0.893 vs 0.902).
Indeed, both models were successful in predicting the activity of the
steroid estrone included in the test set (Table 4). CoMFA model 2 was
developed from the same nine steroids and, in addition, the four
lactones in the training set. The *r*~cv~^2^ statistical parameter for
CoMFA model2, using either *atom-fit* or *flexible field­fit* alignment,
was diminished compared with CoMFA model 1. Nevertheless, it is
noteworthy that *flexible-field fit* fared better than *atom-fit* and
performed better in absolute terms (*r*~cv~^2^ = 0.490 vs 0.442). This
advantage of

*flexible field-fit* over *atom-fit* became more apparent as the size
and structural diversity of the training set were gradually expanded by
inclusion of 5 phytoestrogens (model 3) and, then, by 10 organochlorines
(model 4) (Table 3).

> **Table 3. Summary of Statistical Parameters from CoMFA Models
> 1-5*^a^***

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="2"><blockquote>
<p>model 1</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>model2</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>model 3</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>model4</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>model 5</p>
</blockquote></td>
</tr>
<tr class="even">
<td><em>atom-fit</em></td>
<td><em>field-fit</em></td>
<td><em>atom-fit</em></td>
<td><em>field-fit</em></td>
<td><em>atom-fit</em></td>
<td><em>field-fit</em></td>
<td><em>atom-fit</em></td>
<td><em>field-fit</em></td>
<td><em>atom-fit</em></td>
<td><em>field -fit</em></td>
</tr>
<tr class="odd">
<td><blockquote>
<p>9</p>
</blockquote></td>
<td><blockquote>
<p>9</p>
</blockquote></td>
<td><blockquote>
<p>13</p>
</blockquote></td>
<td><blockquote>
<p>13</p>
</blockquote></td>
<td><blockquote>
<p>18</p>
</blockquote></td>
<td><blockquote>
<p>18</p>
</blockquote></td>
<td><blockquote>
<p>28</p>
</blockquote></td>
<td><blockquote>
<p>28</p>
</blockquote></td>
<td><blockquote>
<p>40</p>
</blockquote></td>
<td><blockquote>
<p>40</p>
</blockquote></td>
</tr>
<tr class="even">
<td><blockquote>
<p>4</p>
</blockquote></td>
<td><blockquote>
<p>4</p>
</blockquote></td>
<td><blockquote>
<p>5</p>
</blockquote></td>
<td><blockquote>
<p>5</p>
</blockquote></td>
<td><blockquote>
<p>5</p>
</blockquote></td>
<td><blockquote>
<p>5</p>
</blockquote></td>
<td><blockquote>
<p>3</p>
</blockquote></td>
<td><blockquote>
<p>3</p>
</blockquote></td>
<td><blockquote>
<p>5</p>
</blockquote></td>
<td><blockquote>
<p>6</p>
</blockquote></td>
</tr>
<tr class="odd">
<td><blockquote>
<p>0.576</p>
</blockquote></td>
<td><blockquote>
<p>0.548</p>
</blockquote></td>
<td><blockquote>
<p>0.442</p>
</blockquote></td>
<td><blockquote>
<p>0.490</p>
</blockquote></td>
<td><blockquote>
<p>0.492</p>
</blockquote></td>
<td><blockquote>
<p>0.565</p>
</blockquote></td>
<td><blockquote>
<p>0.508</p>
</blockquote></td>
<td><blockquote>
<p>0.513</p>
</blockquote></td>
<td><blockquote>
<p>0.446</p>
</blockquote></td>
<td><blockquote>
<p>0.533</p>
</blockquote></td>
</tr>
<tr class="even">
<td><blockquote>
<p>0.893</p>
</blockquote></td>
<td><blockquote>
<p>0.902</p>
</blockquote></td>
<td><blockquote>
<p>0.904</p>
</blockquote></td>
<td><blockquote>
<p>0.944</p>
</blockquote></td>
<td><blockquote>
<p>0.868</p>
</blockquote></td>
<td><blockquote>
<p>0.933</p>
</blockquote></td>
<td><blockquote>
<p>0.920</p>
</blockquote></td>
<td><blockquote>
<p>0.895</p>
</blockquote></td>
<td><blockquote>
<p>0.913</p>
</blockquote></td>
<td><blockquote>
<p>0.960</p>
</blockquote></td>
</tr>
</tbody>
</table>

> *^a^* The size and structural diversity of the training sets increase
> from model 1 through model 5.
>
> **Table 4. Values of Experimentally Observed vs CoMFA-Predicted
> log(RP) for the Test Set Compounds*^a^***

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 15%" />
<col style="width: 9%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 10%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td colspan="2"></td>
<td></td>
<td colspan="3">log(RP) for test set compounds</td>
<td colspan="2"></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td colspan="2"><blockquote>
<p>estrone</p>
<p>obs = 0.98</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>zearalenone</p>
<p>obs = -0.59</p>
</blockquote></td>
<td colspan="2"><p>biochanin A</p>
<p>obs = -2.04</p></td>
<td colspan="2"><p>methoxychlor</p>
<p>obs = -2.48</p></td>
</tr>
<tr class="odd">
<td></td>
<td>no. of compounds</td>
<td></td>
<td>pred</td>
<td>dev<em><sup>b</sup></em></td>
<td>pred</td>
<td><blockquote>
<p>dev<em><sup>b</sup></em></p>
</blockquote></td>
<td><blockquote>
<p>pred</p>
</blockquote></td>
<td><blockquote>
<p>dev<em><sup>b</sup></em></p>
</blockquote></td>
<td><blockquote>
<p>pred</p>
</blockquote></td>
<td>dev<em><sup>b</sup></em></td>
</tr>
<tr class="even">
<td><blockquote>
<p>model 1</p>
</blockquote></td>
<td>9</td>
<td><em>atom-fit</em></td>
<td>0.69</td>
<td>0.29</td>
<td>(-0.72)</td>
<td><blockquote>
<p>(0.13)</p>
</blockquote></td>
<td>(-0.07)</td>
<td>(-1.97)</td>
<td>(-1.43)</td>
<td>(-1.05)</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td><em>field-fit</em></td>
<td>0.47</td>
<td>0.51</td>
<td>(0.81)</td>
<td><blockquote>
<p>(-1.40)</p>
</blockquote></td>
<td>(-0.97)</td>
<td>(1.07)</td>
<td>(-0.39)</td>
<td>(-2.09)</td>
</tr>
<tr class="even">
<td><blockquote>
<p>model2</p>
</blockquote></td>
<td>13</td>
<td><em>atom-fit</em></td>
<td>0.57</td>
<td>0.41</td>
<td>-0.48</td>
<td>-0.11</td>
<td>(0.68)</td>
<td>(-2.72)</td>
<td>(-1.51)</td>
<td>(-0.97)</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td><em>field-fit</em></td>
<td>0.63</td>
<td>0.35</td>
<td>0.35</td>
<td>-0.94</td>
<td>(-0.17)</td>
<td>(-1.87)</td>
<td>(-0.48)</td>
<td>(-2.00)</td>
</tr>
<tr class="even">
<td><blockquote>
<p>model3</p>
</blockquote></td>
<td>18</td>
<td><em>atom-fit</em></td>
<td>0.65</td>
<td>0.33</td>
<td>-0.15</td>
<td>-0.44</td>
<td>-2.09</td>
<td>0.05</td>
<td>(-1.92)</td>
<td>(-0.56)</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td><em>field-fit</em></td>
<td>0.72</td>
<td>0.26</td>
<td>0.39</td>
<td>-0.98</td>
<td>-2.13</td>
<td>0.09</td>
<td>(-0.51)</td>
<td>(-1.97)</td>
</tr>
<tr class="even">
<td><blockquote>
<p>model4</p>
</blockquote></td>
<td>28</td>
<td><em>atom-fit</em></td>
<td>0.22</td>
<td>0.76</td>
<td>-0.08</td>
<td>-0.51</td>
<td>-2.26</td>
<td>0.22</td>
<td>-4.02</td>
<td>1.54</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td><em>field-fit</em></td>
<td>0.44</td>
<td>0.54</td>
<td>0.10</td>
<td>-0.69</td>
<td>-2.21</td>
<td>0.17</td>
<td>-2.14</td>
<td>-0.34</td>
</tr>
<tr class="even">
<td><blockquote>
<p>model5</p>
</blockquote></td>
<td>40</td>
<td><em>atom-fit</em></td>
<td>0.41</td>
<td>0.57</td>
<td>0.55</td>
<td>-1.14</td>
<td>-1.69</td>
<td>-0.35</td>
<td>-2.99</td>
<td>0.51</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td><em>field-fit</em></td>
<td>0.79</td>
<td>0.19</td>
<td>-0.26</td>
<td>-0.33</td>
<td>-1.99</td>
<td>-0.05</td>
<td>-2.75</td>
<td>0.27</td>
</tr>
</tbody>
</table>

*^a^* CoMFA models 1-5, derived from training sets that gradually
increase both in size and in structural diversity (see Table 3), were
constructed using both *atom-fit* and *flexible field-fit* alignment
schemes. *^b^* The deviation (dev) refers to the difference between the
corresponding experimentally observed and CoMFA-predicted log(RP) value
obtained from the *atom-fit* and *flexible field-fit* alignment schemes.
The values highlighted in parentheses refer to predictions made for
compounds that fall outside of the chemical families comprising the
training set used to build the respective CoMFA model.

![](media/image7.png){width="6.6in" height="2.897222222222222in"}

**Figure 2.** Orientation of training set compounds with respect to the
template compound 17*β*-estradiol obtained by *Atom-Fit* alignment (A)
and *Flexible Field-Fit* alignment (B).

As the training set increased in structural diversity, we calculated
log(RP) values for additional test set compounds. The predictive
abilities of these models for the test set proved excellent (Table 4).
For example, the log(RP) value of the organochlorine methoxychlor
accord­ing to experiment is -2.48. The corresponding values predicted by
CoMFA model 4 are -2.82 (-0.34) using *flexible field-fit* and -0.91
(1.57) using *atom-fit,* where the numbers in parentheses refer to the
difference (i.e., residual) between the corresponding experimentally
ob­served and predicted values.

The final CoMFA models, constructed from the entire training set, span
eight structurally diverse chemical classes. Scatter plots of the
experimentally observed

versus CoMFA-calculated log(RP) values for the complete training set of
40 compounds are shown for both the *atom-fit* and *field-fit* alignment
schemes (Figures 3A,B, respectively). Comparison of statistical
parameters as‑

sociated with *flexible field-fit* alignment (*r*~cv~^2^ = 0.533; *r*^2^
= 0.960) and *atom -fit* alignment (*r*~cv~^2^ =0.446; *r*^2^ = 0.913)

reinforces the advantage of field-based alignment over atom-based
alignment for structurally diverse data sets. The results for the test
set are consistent with these conclusions (Table 4). Whereas the largest
residual log-(RP) value using *field-fit* alignment is -0.33
(zearale­none), it is -1.14 (zearalenone) using *atom-fit* alignment. The
residual values given in parentheses in Table 4 correspond to
predictions made for compounds that are

![](media/image8.png){width="5.356944444444444in"
height="2.026388888888889in"}

**Figure 3.** Plot of CoMFA-predicted versus observed values of log(RP)
for the 40 training set compounds. (A) *Atom-Fit;* (B) *Flexible*

*Field-Fit.*

not represented in the training set for a particular model. For example,
CoMFA model2 constructed from steroids and lactones performs poorly in
predicting the log(RP) of methoxychlor (residual = -0.97).
Extrapolation, or predicting values of compounds that fall outside the
"chemical space" of a model, often leads to gross inac­curacies and is
generally unjustified. This caveat under­scores the need for structurally
diverse data sets to build statistically robust and predictive models.

**Discussion**

The present study, in which data sets were systemati­cally expanded by
inclusion of structurally diverse com­pounds spanning eight chemical
families (Figure 1), demonstrates that the choice of initial alignment
strategy is of great importance in achieving optimal 3D-QSAR models
using CoMFA. Another, albeit qualitative, ad­vantage of field-fit over
atom-fit approaches stems from practical difficulties in achieving
atom-based alignments as the structural diversity of the data set
increases. It is intuitively obvious that the process of atom fitting
becomes more problematic as the structural diversity of the data set
increases, and especially when the data set encompasses different
chemical families. In biological applications, this circumstance will
occur for receptors either that are inherently promiscuous with respect
to ligand selectivity or that become promiscuous upon mutation.
Prominent examples ascribing to the first scenario are ER α and *β* and
the steroid and xenobiotic receptor (SXR) *(12,13).* The ERs are
recognized as targets for EDCs, a large and diverse group of exogenous
compounds represented by the present data set. The human SXR and its
pregnane xenobiotic receptor (PCR) orthologue in other mammalian species
are so-called "orphan" nuclear receptors that function as transcription
factors to mediate the metabolism of exogenous chemicals (including
drugs) by activation of the cytochrome P450 enzymes *(24).* A prime
example following the second scenario is the androgen receptor *(25)
(AR),* another nuclear receptor for which key mutations *(26) (e.g.,*
Thr877Ala) within the ligand binding pocket have been associated with
increased affinity for (and activation by) a wider range of endogenous
hormones besides the receptor's natural ligands testosterone and
dihydrotest­osterone (DHT) *(27, 28).* Compelling experimental find­ings
indicate that this particular mutation is associated with the ultimate
failure of androgen ablation therapies for treatment of human prostate
cancer *(29, 30).*

Inspection of the log(RP) values for the test-set com­pounds obtained
from our final CoMFA models (Table 4) reveals that *flexible field-fit*
alignment is predictive equally well for compounds with weak potency
(e.g., methoxychlor) as for strong potency (e.g., estrone). Of note,
both the *atom-fit* and *flexible field-fit* models correctly predicted
the low activational activity of tamox­ifen and 4-hydroxytamoxifen (Table
2) despite their high affinity for ER. It is worth noting that both
alignments made no "false negative" predictions. A false negative refers
to an active (e.g., estrogenic) compound that is predicted as inactive,
whereas a "false positive" refers to an inactive compound predicted as
active. False negatives are especially problematic in risk assessment
scenarios. Unlike the inclusion of a false positive which would
ultimately be excluded with additional testing, "false negative"
compounds that are predicted not to exert endocrine disrupting effects
would receive no further scrutiny insofar as their environmental and/or
toxicologi­cal impact.

In summary, the final CoMFAmodel (model5) is based on a structurally
diverse data set and demonstrates both internal and external predictive
ability regardless of the choice between atom-fit and field-fit
alignment. Never­theless, the present study demonstrates that field-based
alignment is generally preferred over atom-based align­ment as the size
and certainly the structural diversity of the data set increase. This
conclusion is consistent with the CoMFA study by Waller et al. *(10),*
who employed the field-based SEAL alignment scheme for a series of
structural diverse estrogenic compounds. This guidance should prove
useful in developing robust 3D-QSAR models when dealing with
structurally diverse data sets. In the present application on estrogenic
compounds that are known to span several chemical classes, CoMFA models
using field-based alignment approaches are rec­ommended for the
prioritization of chemicals as to their endocrine disrupting effects
prior to in vitro and in vivo screening.

**Acknowledgment.** We thank Dr. Vladyslav Kholodo­vych for his
invaluable assistance in the preparation of the manuscript. W.J.W.
acknowledges financial support for this research from the U.S.
Environmental Protection Agency's Science To Achieve Results (STAR)
program. Although the research described in this article has been funded
in part by the U.S. Environmental Protection Agency's Science To Achieve
Results (STAR) program through Grant GAD R826133, it has not been
subjected

to any EPA review and, therefore, does not necessarily reflect the views
of the Agency, and no official endorse­ment should be inferred.

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> TX0255875