S2.0 S1878535214000173 Main [631781]

ORIGINAL ARTICLE
1st Heterocyclic Update
Cytotoxicity and 2D-QSAR study of some
heterocyclic compounds
M. Abul Kashem Liton *, U. Salma, A. Chandra Bhowmick
Department of Chemistry, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
Received 31 December 2012; accepted 25 January 2014
Available online 4 February 2014
KEYWORDS
2D-QSAR;
MLR;
PLS;
ANN;LOOAbstract Herein we have studied the cytotoxicity and quantitative structure–activity relationship
(QSAR)
of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydan-
toin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC 50
values (Log of LC 50) against brine shrimp lethality bioassay in order to derive the 2D-QSAR
models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and
ANN methods gave highly significant square correlation coefficient ( R2) values of 0.83, 0.81 and
0.91 respectively. The model also exhibited good predictive power confirmed by the high value of
cross validated correlation coefficient Q2(0.74).
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1. Introduction
Hydantoin and thiohydantoin are five-member heterocyclic
system
containing very reactive cyclic urea and thiourea cores
(Lo´pez and Trigo, 1985; Meusel and Gu ¨tschow, 2004 ). In
recent years, heterocyclic compound and its derivatives are
very familiar to their anti-inflammatory ( Ghate et al., 2003 ),
antimicrobial ( Khan et al., 2005 ), antitubercular ( Gupta and
Prabhu, 2004 ), antipyretic ( Shastri et al., 2004 ), analgesic
(Ghate et al., 2005 ), antioxidant ( Torres and Faini, 2006 )
and cytotoxic ( Kostava and Momekov, 2008 ) activities. They
are now widely used as drugs, medicines, dyes and rawmaterials in manufacturing industries etc. Much attention
has been paid to the synthesis of nitrogen (N)-, oxygen (O)-,
sulfur
(S)- containing heterocyclic compounds and their deriv-
atives mainly due to their broad spectrum biological and phar-maceutical activities. In our laboratory, several substituted
heterocyclic compounds such as methyl and bromine groups
in the benzene ring of isatin, D
2-1,3,5-thiadiaozolines were syn-
thesized and found a good cytotoxic activity ( Islam et al.,
2001a, 2001b; Lingcon et al., 2001 ). In light of this biological
activity, we thought of synthesizing different types of hydan-toin and thiohydantoin related heterocyclic compounds and
their derivatives which were tested for cytotoxic activity.
Though development of drugs is lengthy, laborious and expen-sive, computer aided drug design (CADD) can help us to in-
crease the pool of interesting structures that can be
evaluated. The most important step is to find the possiblestructural feature of compounds with desired biological activ-ity. Over the years of development many methods, algorithms
and techniques have been discovered and applied in QSAR*Corresponding author. Tel.: +88 0921 62415.
E-mail address: [anonimizat] (M. Abul Kashem Liton).
Peer review under responsibility of King Saud University.
Production and hosting by ElsevierArabian Journal of Chemistry (2014) 7, 639–646
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Arabian Journal of Chemistry
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http://dx.doi.org/10.1016/j.arabjc.2014.01.014

studies ( He and Jurs, 2005; Eldred et al., 1999 ). Nowadays
QSARs are being applied in many disciplines with much
emphasis in drug design. As a well accepted technique, two-
dimensional quantitative structure–activity relationship (2D-
QSAR) was carried out to study the biological activity. It isa mathematical model that was used to evaluate the toxicity
of a compound from its physiochemical properties of molecu-
lar structures.
2. Experimental
2.1. General methods
All the chemicals and reagents used in these experiments were
pure
and purchased from E-Merck (Germany). Purification
and drying of reagents and solvents were carried out according
to the literature procedure of Armarego and Chai (2003) . Thin
layer chromatographic analysis was performed on E-Merck 60F 254 pre-coated aluminum thin layer chromatographic plates.
Melting points were determined on a Fisher-J ohn’s electro-
chemical melting point apparatus and uncorrected. The IR
spectra were recorded with KBr (Potassium Bromide) disk
on DR-8001, Shimadzu FT-IR spectrometer and
1H NMRspectra were also recorded on a WP 200-NMR spectrometer
using TMS (Trimethylsilane) as an internal standard.
2.2. Synthesis
Twenty two ( 22) compounds were prepared according to the
literature procedure of Muccioli et al. (2003) by direct heating
instead of micro-wave assistance. All the synthesized com-
pounds illustrated in Table 1 were confirmed with melting
point, IR and1H NMR spectral analysis. The synthetic por-
tion was not included in this manuscript due to the main focuson 2D-QSAR study.
2.3. Cytotoxicity calculation
The median lethal concentration (LC
50) with 95% confidence
intervals of the test samples in Table 2 and reference standard
(Vincristine Sulfate) in Table 3 were calculated using the probit
analysis program ( Finney, 1971; Rickman et al., 1974 ) of IBM
SPSS Statistics20 software packages. According to the research
methodology, all experimental LC 50values ( lg/mL) were con-
verted to logarithm of LC 50, i.e., Log 10(LC 50) and used as
dependent variable in 2D-QSAR study.
Table 1 Synthesized compounds which are employed for 2D-QSAR study.
NN
OR1
YX
X
R2
5,5-Di phenyl-imidazolidine-2,4-dione moiet y
Compound ID R1 R2 XY
1 HH H O
2 HC 6H5(phenyl) H O
3 C6H5(phenyl) C6H5(phenyl) H O
4 HC 6H11(cyclohexyl) H O
5 C6H11(cyclohexyl) C6H11(cyclohexyl) H O
6 H H Br O
7 HC H3 Br O
8 HC H3–CH 2 Br O
9 HC H3–CH 2–CH 2–CH 2 Br O
10 HC H3–CH 2–CH 2–CH 2–CH 2 Br O
11 H H Cl O
12 HC H3CH 2 Cl O
13 HC H3–CH 2–CH 2–CH 2 Cl O
14 HC H3–CH 2–CH 2–CH 2–CH 2–CH 2–CH 2 Cl O
15 HH H S
16 HC H3 HS
17 CH 3 CH 3 HS
18 HC H3CH 2 HS
19 CH 3CH 2 CH 3CH 2 HS
20 HC H3CH 2CH 2CH 2 HS
21 CH 3CH 2CH 2CH 2 CH 3CH 2CH 2CH 2 HS
22 HC 6H5(phenyl) H S640 M. Abul Kashem Liton et al.

2.3.1. Test animal
Brine shrimps were used as test animal for the investigation of
cytotoxic
activity ( Martin et al., 2008; Islam et al., 2009; Wang
et al., 1998 ) and its scientific name is Artemia Salina .
2.3.2. Hatching of shrimp
Brine shrimp ( A. Salina ) eggs were hatched in a vessel contain-
ing sterile artificial seawater prepared by dissolving 38 g of ta-
ble salt in 1L distilled water. The vessel was kept under aninflorescent bulb and facilitated with good aeration for 48 h
at room temperature. After hatching, nauplii released from
the egg shells were collected at the bright side of the vessel(near the light source) by using micropipette. The larvae wereisolated from the eggs by aliquoting them in small beaker con-
taining the seawater.
2.3.3. Brine shrimp lethality bioassay
The brine shrimp lethality bioassay was used to predict the
cytotoxic
activity ( McLaughlin et al., 1998; Middleton et al.,
2005) of the compounds. For the experiments, 4 mg of each
test sample was dissolved in dimethyl sulfoxide (DMSO) andsolutions of varying concentrations (100, 50, 25, 10, 5, and 2lg/mL)
were obtained by the serial dilution technique using
simulated seawater. The solutions were then added to the
pre-marked glass vials containing 20–25 live brine shrimp nau-plii in 10 mL simulated seawater. After 24 h, the vials were in-
spected using a magnifying glass, and the number of survived
nauplii in each vial was counted. The mortality endpoint ofthis bioassay was defined as the absence of controlled forward
motion during 30 s of observation ( Meyer et al., 1982 ). From
this data, the percent of lethality of the brine shrimp nauplii
for each concentration and control was calculated. Vincristine
Sulfate (reference standard) and DMSO were used as positive
control and negative control respectively. All the procedureswere replicated three times.
3. Calculating descriptors
All the calculations were performed on Intel core-i5 Fujitsu
Laptop
Computer with 6–8 GB of memory and 100 GB of
scratch disk space. The 3D-structure of 22 molecules was
sketched by the Gauss view03 software. The MolecularMechanics (MM+) force field with the aid of Chemoffice Ul-
tra-5 software package was applied for preliminary geometry
optimization. The final geometry optimization of the MM+force field optimized structures was done by Gaussian98
(Revision A.9) ( Frisch et al., 1998 ) software applying
B3LYP/6-31G(d,p) level of theory. Semi-empirical PM3 ( Stew-
art, 1989a, 1989b ) method by Gaussian98 software had been
used in order to obtain an accurate charge distribution and
quantum-chemical descriptors (Net atomic charge, Q
A; mean
absolute atomic charge, QM; Highest occupied molecular orbi-
tal, HOMO; Lowest unoccupied molecular orbital, LUMO;Energy gap, E
GAP; Absolute hardness, g; Activation hardness,
Dg;relectron densities, Qr;pelectron densities, Q p; Molecular
polarizability, a; Ionization energy, IP; Total energy, ET; HeatTable 2 Cytotoxic activity of the synthesized compounds against brine shrimp nauplii.
Compound ID Concentration tested ( lg/mL) Probit LC50(lg/mL) Log 10(LC 50) 95% Confidence interval
1 2, 5, 10, 25, 50, 100 3.72, 3.92, 4.12, 4.48, 4.77, 5.05 95.59 1.98 62.90–177.60
2 2, 5, 10, 25, 50, 100 4.29, 4.48, 4.67, 4.85, 5.15, 5.36 31.50 1.50 21.64–52.65
3 2, 5, 10, 25, 50, 100 5.11, 5.62, 5.77, 6.23, 6.56, 7.33 1.60 0.20 0.89–2.35
4 2, 5, 10, 25, 50, 100 3.82, 4.01, 4.19, 4.56, 4.82, 5.10 86.00 1.93 55.73–156.01
5 2, 5, 10, 25, 50, 100 5.00, 5.50, 5.77, 6.13, 6.41, 7.05 1.90 0.28 1.14–2.77
6 2, 5, 10, 25, 50, 100 3.87, 4.05, 4.23, 4.59, 4.85, 5.10 82.00 1.91 53.64–153.68
7 2, 5, 10, 25, 50, 100 4.23, 4.39, 4.59, 4.80, 5.08, 5.33 40.00 1.6 27.03–69.39
8 2, 5, 10, 25, 50, 100 4.26, 4.45, 4.64, 4.82, 5.13, 5.36 34.00 1.53 23.21–56.31
9 2, 5, 10, 25, 50, 100 4.87, 5.15, 5.33, 5.58, 5.77, 6.04 3.10 0.49 1.53–4.92
10 2, 5, 10, 25, 50, 100 4.98, 5.36, 5.61, 5.92, 6.18, 6.56 2.10 0.32 1.14–3.16
11 2, 5, 10, 25, 50, 100 3.77, 3.96, 4.16, 4.53, 4.77, 5.08 91.25 1.96 59.89–170.43
12 2, 5, 10, 25, 50, 100 4.64, 4.85, 4.95, 5.23, 5.44, 5.67 9.40 0.97 5.76–13.80
13 2, 5, 10, 25, 50, 100 4.82, 5.13, 5.31, 5.52, 5.74, 5.99 3.55 0.55 1.82–5.53
14 2, 5, 10, 25, 50, 100 5.18, 5.74, 6.04, 6.34, 6.75, 7.33 1.30 0.11 0.70–1.96
15 2, 5, 10, 25, 50, 100 4.16, 4.36, 4.56, 4.77, 5.05, 5.28 43.82 1.64 29.59–75.96
16 2, 5, 10, 25, 50, 100 4.08, 4.23, 4.42, 4.67, 4.95, 5.18 63.60 1.8 41.19–122.47
17 2, 5, 10, 25, 50, 100 3.92, 4.08, 4.26, 4.62, 4.92, 5.18 76.30 1.88 50.16–142.11
18 2, 5, 10, 25, 50, 100 4.42, 4.64, 4.80, 5.00, 5.28, 5.52 18.75 1.27 12.93–28.20
19 2, 5, 10, 25, 50, 100 4.72, 4.95, 5.08, 5.33, 5.52, 5.77 6.50 0.81 3.63–9.64
20 2, 5, 10, 25, 50, 100 4.59, 4.80, 4.90, 5.15, 5.41, 5.64 11.20 1.05 7.30–16.61
21 2, 5, 10, 25, 50, 100 4.82, 5.13, 5.31, 5.55, 5.74, 5.99 3.52 0.55 1.81–5.48
22 2, 5, 10, 25, 50, 100 4.85, 5.15, 5.33, 5.58, 5.77, 6.04 3.24 0.51 1.64–5.07
Table 3 Cytotoxicity of the reference standard (Vincristine
Sulfate) on brine shrimp nauplii.
Concentration
tested ( lg/mL)Probit LC 50
(lg/mL)Log 10
(LC 50)95% Confidenceinterval
2 4.77 2.99 0.48 2.20–3.80
5 5.31
10 5.7425 6.34
50 6.75
100 7.33Cytotoxicity and 2D-QSAR study of some heterocyclic compounds 641

of formation, DHF; Steric energy, ES; Molecular surface area,
MSA; Molecular volume, MV; Molecular dipole moment,
MDM etc.) for each compound in the series and some other
descriptors (Gibbs’s free energy change of solvation, dG/C14
solv;
electrostatic Gibb’s free energy change in solution, dG/C14
elec
etc.) were also calculated by Gaussian98 at B3LYP/6-31G(d,p)-CPCM solvation (DMSO) method. Initially, a totalof twenty five types of semi-empirical and thermodynamic
descriptors were calculated and these descriptors were finally
reduced into four ( Table 4) to study the 2D-QSAR models.
The MLR, PLS and ANN methods were applied to performthe QSAR models and cross validation method (LOO) was
also used to validate the predictive ability of the model ob-
tained by MLR method.
4. Statistical methods
For the QSAR study, model selection was performed by Build
QSAR
(version 2.1.0.0) ( De Oliveira and Gaudio, 2001 ) soft-
ware program. Statistical calculations allowed for the selection
of the models with the following characteristics: high squaredcorrelation coefficient ( R
2), high Fischer’s value ( F-test), low
standard error of estimate (SEE), correlation matrix ( Table 5)
among the parameters and the least number of descriptors in-volved. Next, the IBM SPSS Statistics20 software packages were
applied for detailed statistical analysis of the QSAR models.5. Results and discussions
The correlation between various descriptors ( Kier
and Hall,
1986, 1999 ) with biological activity is the most important
means of structure–activity relationship (SAR) study. Thus
the equation should use the minimum number of descriptors
to obtain the best fit. To achieve this, a popular procedure is
used to find out the saturation point, a point beyond whichthere is no considerable improvement in regression coefficient(R
2) values has been observed. By interpreting the resulting
descriptors, it is possible to gain some insight into factors thatare likely to govern the cytotoxic activity. The best QSARmodel built using multiple linear regression (MLR) method
is represented by the following equation:Table 4 Short explanation of the descriptors used to establish the 2D-QSAR model.
Full name Description Abbreviation
Highest occupied
molecular orbitalIt has been shown Zhou and Parr (1990) that this orbital plays a major role in
governing many chemical reactions and determining electronic band gaps in
solids; they are also responsible for the formation of many charge transfer
complexes Franke (1984), Osmialowski et al. (1985) . The energy of the HOMO
is directly related to the ionization potential and characterizes the susceptibility
of the molecule toward attack by electrophiles. Hard nucleophiles have a low-
energy HOMO, soft nucleophiles have a high energy HOMOHOMO
Heat of formation The energy liberated or absorbed when one mole of a compound is formed from
its constituent elements. In common usage, the heat of formation is used inplace of the more precise term the enthalpy of formation, which has the symbol,DH
F. The reaction enthalpy can be accounted for by the difference in heats of
formation between reactants and products or between conjugated species
Gruber and Buss (1989), Shusterman (1991), Debnath et al. (1994)DHF
Total energy The total energy has been used as a measure of nonspecific interactions between
a solute and stationary phase in gas-chromatography Osmialowski et al. (1986) .
The energy of protonation, defined as the difference between the total energies
of the protonated and neutral forms of the molecule, can be considered as agood measure of the strength of hydrogen bonds (the higher the energy, the
stronger the bond) and can be used to determine the correct localization of the
most favorable hydrogen bond acceptor site Trapani et al. (1993)E
T
Steric energy Molecular mechanics assumes the steric energy of a molecule to arise from a
few, specific interactions within a molecule. These interactions include thestretching or compressing of bonds beyond their equilibrium lengths and angles,torsional effects of twisting about single bonds, the Van der Waals attractionsor repulsions of atoms that come close together, and the electrostatic
interactions between partial charges in a molecule due to polar bonds. To
quantify the contribution of each, these interactions can be modeled by apotential function that gives the energy of the interaction as a function of
distance, angle, or charge Hehre (2003), Cornell et al. (1995)ES
Table 5 Correlation matrix among the descriptors.
HOMO DHF ET ES
HOMO 1
DHF 0.786 1
ET 0.235 0.502 1
ES 0.885 0.848 0.565 1642 M. Abul Kashem Liton et al.

log10ðLC50Ț¼0:7752ð/C61:0023ȚHOMO
ț0:0023ð/C60:0069ȚDHF
ț0:0016ð/C60:0005ȚET
ț0:0096ð/C60:0062ȚESț11:5616ð/C69:1645Țð1Țðn¼22;R¼0:91;R2¼0:83;SEE¼0:31;F¼20:70;p
<0:0001 ;Q2¼0:74;SPRESS ¼0:38;SDEP ¼0:34Ț
where, nis the number of observations, Ris the correlation
coefficient, R2is the squared correlation coefficient, SEE is
the standard error of estimate, pis the statistical significance
>99.9% with Fisher’s statistic F,SPRESS is the standard devi-
ation of sum of squared error of prediction and SDEP is the
standard deviation of error of prediction.Table 6 TheR2
trainandQ2
LOOvalues after several Y-random-
ization tests.
No of Yrand R2
train Q2
LOO
1 0.1325 0.0236
2 0.0259 0.0116
3 0.0502 0.0092
4 0.0552 0.0217
5 0.1190 0.01456 0.0376 0.0221
7 0.1082 0.0359
8 0.1169 0.04759 0.05182 0.0154
10 0.0488 0.0123
Table 7 Prediction of cytotoxic activity of test set compounds
in five cross validation cycles (leave-5-out) based on the
descriptors set of Eq. (1).
Cycle Test set Training set R2
cvðextȚ
1 1, 6, 11, 16, 21 Rest of the compounds, n= 17 0.66
2 2, 7, 12, 17, 22 Rest of the compounds, n= 17 0.71
3 3, 8, 13, 18, 1 Rest of the compounds, n= 17 0.73
4 4, 9, 14, 19, 2 Rest of the compounds, n= 17 0.75
5 5, 10, 15, 20, 3 Rest of the compounds, n= 17 0.70
Figure 1 Predicted cytotoxic activities by MLR in comparison
with experimental.
Figure 2 Predicted cytotoxic activities by PLS in comparison
with experimental.
Figure 3 Predicted cytotoxic activities by ANN in comparison
with experimental.
Figure 4 Predicted cytotoxic activities by cross validation (LOO)
in comparison with experimental.Cytotoxicity and 2D-QSAR study of some heterocyclic compounds 643

The high correlation coefficient R(0.91) indicates the sus-
ceptibility of descriptors (HOMO, DHF,ETandES) to form
the above model (1). Squared correlation coefficient ( R2)o f
0.83 explains 83% variance in biological activity of the tested
compounds. It also indicates the statistical significance>99.9% with F-values (20.70). Cross-validated square correla-
tion coefficient ( Q
2) by LOO technique was 0.74 which showed
a good internal predictive ability of the model. The model wasalso validated by applying the Y-randomization test. Severalrandom shuffles of the Yvector
were performed and the ob-
tained results are in good agreement with the suggested limits(Eriksson
et al., 2003 ). The low R2
trainandQ2
LOOvalues shown
inTable 6 indicate that there is no chance of correlation or
structural dependency in the proposed model. Consequently
Eq.(1)can be considered as a perfect model with both high
statistical significant and excellent predictive ability. The pre-dictive ability of the model was further confirmed by leave-5-out cross validation with R
2
cvðextȚ. The R2cvðextȚvalues are shown
inTable 7. It is observed that HOMO, DHF,ETandESare the
best descriptors in the establishment of the QSAR model for
heterocyclic derivatives such as hydantoin and thiohydantoinrelated compounds. The correlation of the experimental activ-
ities with the MLR calculated ones is illustrated in Fig. 1. Par-
tial Least Square (PLS) was also applied to generate a model
(Geladi and Kowalski, 1986; Cramer et al., 1988 ) for quantita-
tive structure–activity relationship (QSAR) between a set ofmolecular descriptors used in the MLR method and experi-mental activity. The correlation of the experimental and calcu-
lated activities with the PLS method is shown in Fig. 2. The
correlation coefficient R(0.90), squared correlation coefficient
R
2(0.81), standard error of estimate SEE (0.32), and Fischer
Statistics F(18.55) obtained with the PLS method indicate that
the model proposed to predict activity is significant and perti-
nent to that of MLR method. In addition the architecture 5-5-1 of three layer artificial neural networks (ANN) shown in
Fig. 5 is used to calculate the biological activities with the help
of a set of molecular descriptors and experimental activity. The
correlation between experimental and calculated activities with
the ANN method is shown in Fig. 3 . The correlation coeffi-
cient R(0.95), squared correlation coefficient R
2(0.91), stan-
dard error of estimate SEE (0.23), and Fischer Statistics F
(41.94) obtained with the neural network show that model pro-
posed to predict activity by ANN was good and relevant tothat of MLR method. The correlation of the experimental val-
ues with the calculated values in LOO procedure shown in
Fig. 4 was reliable due to the high values of the correlation
coefficient R
CV(0.86), squared correlation coefficient R2
cv
(0.74), standard error of estimate SEE (0.40) and Fischer
Statistics F(14.65).
Over all, the biological activities predicted by MLR, PLS,
ANN and LOO procedure with respect to their experimentalTable 8 Experimental cytotoxic activities and predicted
activities by MLR, PLS, ANN and cross validation (LOO)
methods.
Compound ID Log (LC 50)
Exp. MLR PLS ANN Cross validation
(LOO)
1 1.98 2.45 2.40 2.04 2.78
2 1.50 1.36 1.43 1.42 1.39
3 0.20 0.20 0.35 0.05 0.334 1.93 1.33 1.26 1.40 1.365 0.28 0.30 0.20 0.20 0.45
6 1.91 1.72 1.80 1.89 1.76
7 1.60 1.61 1.76 1.70 1.71
8 1.53 1.24 1.31 1.52 1.38
9 0.49 0.75 0.84 0.65 0.69
10 0.32 0.53 0.61 0.32 0.46
11 1.96 1.63 1.60 1.94 1.54
12 0.97 1.46 1.38 1.49 1.70
13 0.55 0.71 0.67 0.58 0.80
14 0.11 /C00.01 /C00.03 0.18 /C00.18
15 1.64 1.77 1.78 1.87 1.92
16 1.80 1.61 1.59 1.76 1.60
17 1.88 1.56 1.49 1.64 1.72
18 1.27 1.34 1.31 1.35 1.45
19 0.81 1.17 1.05 1.01 1.18
20 1.05 0.84 0.83 0.82 0.64
21 0.55 0.57 0.43 0.61 0.72
22 0.51 0.70 0.89 0.71 0.99
Exp.: Experimental, MLR: Multi Linear Regression, PLS: Partial
Least Square, ANN: Artificial Neural Network, LOO: Leave One
Out.
Figure 5 Schematic representation of architecture (5-5-1) of the three layer neural network (ANN).644 M. Abul Kashem Liton et al.

values are shown in Table 8. The predicted activities in Fig. 6
show the approximately same approaches to the experimental
values.
6. Conclusion
The brine shrimp lethality bioassay is considered as a useful
tool
for rapid and preliminary assessment of toxicity of the
compounds. Further studies are required to calculate the more
accurate bioactivity and, to find the mode of pharmacologicalactivities. Significant regression equations were obtained byMLR, PLS and ANN methods with respect to their experi-
mental cytotoxic activities. The best regression equation was
obtained on the following descriptors: Highest occupiedmolecular orbital (HOMO), heat of formation ( DH
F), total en-
ergy ( ET) and steric energy ( ES). These variables allowed phys-
ical explanation of electronic molecular propertiescontributing to the cytotoxic activity as the electronic charac-
ter relates directly to the electron distribution of interacting
molecules. The predicted biological activities by MLR, PLSand ANN showed a good agreement with experimental values
but the activities obtained from ANN were relatively better
among them. The LOO, Leave-5-out cross validation and theY-randomization techniques indicate that the model is signifi-
cant, robust and has a good predictive ability.
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