Study of electronic and structural features of thiosemicarbazone and thiosemicarbazide derivatives...

Study of electronic and structural features of thiosemicarbazoneand thiosemicarbazide derivatives demonstrating

anti-HSV-1 activity

Ah. Altuna,*, M. Kumrua, A. Dimoglob

aPhysics Department, Faculty of Arts and Sciences, Fatih University, 34900, B. Cekmece, Istanbul, TurkeybInstitute of Technology, PK 141, 41400, Gebze, Turkey

Received 20 December 1999; revised 13 April 2000; accepted 8 May 2000

Abstract

The structure±activity relationships (SAR) in a series of thiosemicarbazone and thiosemicarbazide derivatives (60

compounds) have been investigated by means of the Electron-Topological Method (ETM). All derivatives in the training

set are potent inhibitors of herpes simplex virus type 1 (HSV-1) replication (C. Shipman, S.H. Smith, J.C. Drach, D.L. Klayman,

Antiviral Research, 6 (1986) 197±222). On the basis of geometrical and electronic data calculated for each compound and

arranged in a matrix form, two features of activity and three breaks of activity have been revealed. By means of the method of

multivariable regression a quantitative model was built to express the activity dependence on some physicochemical and

structural parameters. q 2001 Elsevier Science B.V. All rights reserved.

Keywords: Structure±activity relationships (SAR); Electron topological method (ETM); Herpes simplex virus (HSV); Inhibitory activity

1. Introduction

In 1950 Hamre et al. [2] found that derivatives of

benzaldehyde thiosemicarbazone were active against

neurovaccinial infection in mice when given orally. It

was the ®rst study on the antiviral activity of thiose-

micarbazones that prompted further investigation of

their properties. The thiosemicarbazone of isatin was

found strongly active [3]. A clinical trial of the

N-methyl derivative of isatin-b-thiosemicarbazone

(methisazone) was carried out in India [4±6]. These

studies have been widely accepted as evidence of the

effective antiviral activity of methisazone in humans

[7]. Even though the drug has been used to treat

patients with genital lesions caused by herpes simplex

virus (HSV), it had little effect on the severity or

duration of the lesions [8].

Sidwell and co-workers evaluated a series of purine

analogs as antiviral agents [9]. This was the ®rst report

of a substituted thiosemicarbazone being active

against a herpes virus. Brockman and co-workers

examined the effect of heterocyclic thiosemicarba-

zones such as pyridine, isochinoline, purine, and isatin

derivatives on HSV. They found that only those

compounds were active in which the thiosemicar-

bazide moiety was af®xed to the heterocyclic ring

in the alpha position relative to the ring nitrogen

[10].

The synthesis of new compounds that can be used

as drugs in everyday life is one of the most important

tasks of molecular design. Detailed considerations

Journal of Molecular Structure (Theochem) 535 (2001) 235±246

0166-1280/01/$ - see front matter q 2001 Elsevier Science B.V. All rights reserved.

PII: S0166-1280(00)00582-0

www.elsevier.nl/locate/theochem

* Corresponding author.

E-mail address: [email protected] (Ah. Altun).

limited by the expenses required for experts,

laboratory equipment and millions of animals used

in experiments are necessary when testing the

compounds on bio-receptors. That is why computer-

ized approaches are widely used for biological activ-

ities investigation at present. The so called

ªStructure±Activity Relationship (SAR)º studies are

mainly based upon establishing correlation between

structures of compounds and their activities, compu-

ter-assisted screening and the activity prognostication,

prior to experiments [11,12].

In this study, a series of thiosemicarbazones of 2-

acetylpyridine, 2-acetylquinoline, 1-acetylisoquino-

line, and related compounds (Tables 1 and 2) have

been investigated as inhibitors of herpes simplex

virus-1 (HSV-1) by means of ETM for SAR

studies.

2. Materials and methods

SAR investigations were performed with the aid of

a modi®ed version of the Electron Topological

Method (ETM)[13±17]. ETM deals with three-

dimensional discrete characterizations of molecular

structures. When compared with the majority of

other approaches to SAR study, ETM shows the

following advantages:

1. ETM is capable of processing series of compounds

with quite diverse structures.

2. ETM estimates the activity qualitatively, but quan-

titative estimation can be also done after this, if

needed. Meanwhile in the frameworks of the

other SAR methods just quantitative study can be

applied.

Ah. Altun, et al. / Journal of Molecular Structure (Theochem) 535 (2001) 235±246236

Table 1

Skeleton types of the compounds in the training set

3. Instead of using integral characteristics of

compounds such as molecular weight, solubility,

lipophilicity, etc. ETM pays primary attention to

their electronic structure. It uses physical±chemi-

cal and quantum-chemical characteristics alone

with conformational analysis data, which are

arranged in the form of n £ n matrices. The latter

are called Electron Topological Matrices of

Conjunction (ETMC) and serve as a language for

the compound structure description.

To form an ETMC, a number of selections are to be

made

1. A property for atoms (bond order, atomic charge,

valance activity, polarizability, HOMO or LUMO

energy, etc.) is to be chosen. The corresponding

values are used as diagonal elements aii.

2. A property for bonds is to be ®xed for off-diagonal

elements. The off-diagonal elements, aij, are of two

kinds.

(a) If i and j label two chemically bonded atoms,

then aij can be one of electronic parameters of

the (i±j)-bond, such as bond order (Wiberg's

index [18]), bond energy (total, covalent or

ionic), polarizability and so on.

(b) If i and j label two nonbonded atoms, then aij

is the interatomic distance.

Since each ETMC is symmetric with respect to its

diagonal elements, only the upper half of it is kept in

the memory of the computer and processed. By

comparing ETMC of one of the most active

compounds (the template compound) with other

ETMCs, we obtain features responsible for the activ-

ity as submatrices of the template, that are called

Electron Topological Submatrices of Activity

(ETSA). They represent fragments of real molecular

structures.

Flexibility of molecules is taken into account by

setting initial parameters for the comparison, such as

D 1 for atoms and D 2 for bonds. These parameters are

used to establish the similarity of atoms and bonds in

molecules under comparison. Sometimes D 3 can be

used for distances at place of D 2.

The ®tness of the features found is determined with

respect to two probabilistic estimations known from

literature, that are given below

Pa � n1 1 1

n1 1 n3 1 2; aa � �n1ń4 2 n2ń3��

N1Ń2Ń3Ń4

p

Here n1 and n2 are the numbers of molecules that

include and do not include, respectively, the features

of the activity selected by the ETM in the class of

active compounds. n3 and n4 have the same meaning

for the class of inactive ones. N1 and N2 are the

numbers of molecules in the classes of active and

inactive compounds, respectively; N3 � n1 1 n3;

N4 � n2 1 n4: In this way, Pa evaluates the deposit of

only active molecules, while a a re¯ects the deposit of

both active and inactive compounds in the feature of the

activity found (for this reasona a is always less than Pa in

most cases). It is quite close to 1 only in one

Ah. Altun, et al. / Journal of Molecular Structure (Theochem) 535 (2001) 235±246 237

Table 2

Some substituents of the compounds in the training set

ªidealº case, namely, when all active compounds

and none of inactive ones include the feature

under estimation.

To ®nd the features of activity, the steps listed

below are to be carried out [19,20]. By this, it is

presupposed that we have a proper series of

compounds, active alone with inactive ones, whose

structures are known, activities evaluated quantita-

tively (or, at least, qualitatively as 1 or 0). The steps

are:

1. Make quantum chemistry calculations and geo-

metry optimization for a training set.

2. Fix a property for atoms and a property for bonds.

Form ETMC for every compound (active or inac-

tive), based on the results obtained at Step 1.

3. Choose a template compound (one of the most

active ones, if known).

4. Choose desirable level of activity to split all the

series into classes of activity (initially into two

classes, but, possibly, more than two) and para-

meters D 1 and D 2.

5. Compare the template chosen with the rest of

molecules. If the features selected are not represen-

tative enough, change some of the initial para-

meters or all of them (the template, D i, level of

activity, properties for atoms and bonds).

6. After obtaining satisfactory features, take a testing

set of molecules and repeat all previous steps. If the

same features of the activity are found with the

same values of probabilities as at the last step of

the training set investigation, the features are


Table 3

The constituents varied and viral replication values for the corresponding molecules [1]

N Skel. type R1 R2 C mg/ml N Skel. type R1 R2 C mg/ml

1 A1 H H 1.20 31 A3 B6 ± 0.57

2 A1 CH3 H 0.14 32 A4 NH±C2H5 ± 5.60

3 A1 C2H3yCH2 H 0.70 33 A4 NH±C3H7(n) ± 0.60

4 A1 C(CH3)2±CH2±C(CH3)2 H 1.00 34 A4 N(i-C4H9)2 ± 2.40

5 A1 CH2±[CH(OH)]4±CH2OH H 5.40 35 A4 N(CH3)±C6H4±OCH ± 0.19

6 A1 CH2±C6H5 H 0.10 36 A4 B4 ± 0.87

7 A1 CH2±o(CH3)±C6H4 H 0.29 37 A4 B5 ± 0.15

8 A1 CH2±o(C5H5N) H 0.37 38 A5 N(CH3)2 ± 0.11

9 A1 B1 H 4.60 39 A5 N(CH3)±C6H11 ± 1.90

10 A1 m(F)±C6H4 H 0.26 40 A5 B9s ± 0.15

11 A1 p(CF3)±C6H4 H 0.24 41 A5 pCH3±B2 ± 1.50

12 A1 CH3 CH3 0.08 42 A5 B10 ± 0.43

13 A1 CH3 C6H11 0.22 43 A5 B4 ± 2.20

14 A2 m(CH2OH)±B2 CH3 0.46 44 A5 B6 ± 0.33

15 A2 B3 CH3 0.85 45 A6 NH±C3H7(n) ± 0.37

16 A2 B4 CH3 0.18 46 A6 B9 ± 0.04

17 A2 B5 CH3 0.21 47 A6 B5 ± 0.22

18 A2 B6 CH3 0.49 48 A7 NHCH2±C6H5 ± 8.50

19 A2 B7 CH3 0.20 49 A7 B11 ± 0.32

20 A2 B8 CH3 0.28 50 A7 B12 ± 0.35

21 A2 NHCH2CHyCH2 C2H5 0.58 51 A7 B5 ± 0.17

22 A2 N(CH3)2 C2H5 0.70 52 A8 B9 ± 0.30

23 A2 B4 C2H5 0.10 53 A8 B5 ± 0.28

24 A2 B6 C2H5 0.29 54 A9 NH2 O 3.90

25 A2 NHCH2CHyCH2 C3H7 0.60 55 A9 NH±C6H5 O 7.00

26 A2 NHCH2CHyCH2 CH(CH3)2 2.00 56 A9 NH±C6H5 Se 0.18

27 A2 B4 CH(CH3)2 0.10 57 A9 B6 Se 0.20

28 A2 B6 CH(CH3)2 0.04 58 A10 ± ± 0.12

29 A3 NHCH2CHyCH2 ± 1.20 59 A11 ± ± 3.20

30 A3 B4 ± 1.70 60 A12 ± ± 0.56

considered appropriate for the activity prediction.

If not, then the more profound study of the features

violation is to be undertaken, and all the steps are to

be repeated again. If no testing series, the sliding

examination can be carried out instead.

The procedure for searching features of inactivity,

or ªbreaks of activityº, is the same as the one for the

features of activity selection. The only difference is

that the template compound is an inactive one.

In this study, the compounds whose viral replica-

tion values are smaller than 0.4 have been presup-

posed to be active. The training set (see Table 3)

includes 32 active and 28 inactive compounds. A

molecular mechanics program (MMX) [21] and a

semi-empirical quantum-chemical approach (CNDO/

2) [22,23] were used to determine structural and elec-

tronic parameters for each compound in the series.

Their results were used to form electron-topological

matrices of conjunction (ETMC). To validate the

features obtained, the sliding examination has been

carried out.

3. Results and discussion

Each ETMC was formed of effective charges on


Fig. 2. The feature of activity AF-I and the corresponding ETSA

Fig. 1. Conformational energies of N12 dependent on the angles of rotation w i.

atoms (Qii), the Wiberg's indices (Wij) for chemically

bonded atoms, and optimized distances between

chemically nonbonded atoms in molecules (Rij). The

electronic charges are given in electronic charge unit

e, and the distances are given in AÊ .

Structures of the molecules under study were opti-

mized, and the rotation of separate groups of atoms

around the most labile bonds was taken into account

to make an unambiguous choice among possible

conformations. As an example, dependence of the

rotation energies on the values of rotation angles

(w 1±w 5) is shown in Fig. 1 for the molecule N12.

For w 2 and w 5 at 1808 there exists a minimum close

to the minimum at 08. The w 5 rotation of 1808 results

in the same conformation since each CH3 replaces the

other one. The w 2 rotation proceeds under high values

of energy of the internal rotation barrier. Hence, the

energy of the given conformation is less than the one

obtained when w 2 rotation is performed with 1808.Since the rotation for w 3 is suppressed, it is more

pro®table to leave the system in its initial position.

For the w 1 and w 4 rotations, the given conformation

is more stable again.

After comparing all the compounds with template


Fig. 4. Graphical representation of the dependence between Pa and D 2 when D1� 0.0499.

Fig. 3. Graphical representation of the dependence between Pa and D 1 when D2� 0.15.

compounds, we revealed two features of anti-HSV-1

activity. When N12 is taken as a template compound,

the feature of activity-I (AF-1) is found (see Fig. 2).

By this, the best values for D 1 and D 2 are 0.0495

and 0.155, respectively. Within the limits given, the

feature responsible for the activity is found in 25

active and 4 inactive compounds. Hence, a a and Pa

are equal to 0.47 and 0.84, respectively.

D 1 and D 2 play an important role for the features

selection. For AF-I, the effect of varying D 1 under a

®xed value of D 2 on the Pa estimate is shown graph-

ically in Fig. 3.

In a similar manner, the relation between Pa and D 1

can be seen from Fig. 4.

For small D 1 and D 2 the values of n1 and n3 are

minimal. After reaching the optimum values or ranges

of D 1 and D 2, their further growth causes the decrease

of the probability of a feature realization. It happens

because of the compounds from inactive class getting

the feature. It means that Pa diminishes while n3

grows.

Extremely ¯exible molecules, i.e. the ones that are

capable of forming additional intramolecular bonds,

can have a few local minima. Hence, one molecule

can be found in a few stable conformations. In such

cases the limits of variation for distances go out of the

limits allowable for D 2. That is why any such confor-

mation is to be considered as a separate molecule and

included into the series given as a self-standing

compound. If later, one of the conformations gets


Fig. 6. IF-1 and the corresponding Electron Topological Sub-matrix of Inactivity (ETSI).

Fig. 5. The feature of activity AF-2 and the corresponding ETSA.

the feature of activity while others do not possess the

feature, it means that this active conformation is char-

acteristic of the molecule in reality.

When N28 is taken as the template compound,

another feature responsible for the activity demon-

stration (AF-2) that is formed by four chemically

non-bonded atoms (see Fig. 5) is found. The best D 1

and D 2 for this feature are equal to 0.044 and 0.100,

respectively. This feature enters just 12 active

compounds. Hence, a a and Pa are 0.64 and 0.93,

respectively.

Some compounds in which the thiosemicarbazide

moiety is af®xed to the heterocyclic ring in the b- and

g-position relative to the ring nitrogen were also of

interest for us. Since these compounds include none of

the activity features shown above, they are inactive in

accordance with the results obtained by Brockman

and co-workers [10].

After determining activity features, it may be seen

that some inactive molecules contain activity features.

It means that the presence of the activity features is a

necessary but not suf®cient condition for activity

demonstration. The question whether these

compounds contain features of inactivity or not,

should be analyzed for the more complete modeling.

After taking different inactive compounds as


Fig. 8. IF-3 and the corresponding ETSI.

Fig. 7. IF-2 and the corresponding ETSI.

templates, three features of inactivity have been

revealed.

Inactivity feature IF-1 is found when the molecule

N55 is chosen as the template compound (see Fig. 6).

The most satisfactory result is obtained when

D 1� 0.05 and D 2� 0.15. This feature is found in 12

inactive compounds of the training set. None of the

active compounds includes this feature. As a result,

probabilistic estimates a a and Pa are 0.56 and 0.93,

respectively.

Inactivity feature IF-2, found when the template

compound is N32, enters just 9 inactive compound

(see Fig. 7). Hence, the probabilistic estimations a a

and Pa are 0.45 and 0.91, respectively. D 1 and D 2

appeared to be the same as in IF-1.

The third inactivity feature (IF-3) is found when the

template compound is N15 (see Fig. 8). It enters just 7

inactive compounds. D 1 and D 2 are again as in IF-1.

Probabilistic estimations a a and Pa are 0.4 and 0.89,

respectively.

Shipman et al. [1] have demonstrated that the

azomethine bond reduction in a molecule (i.e. conver-

sion of a thiosemicarbazone to thiosemicarbazide) did

not produce a proportional decrease in the antiviral

activity. With the activity features found as a result

of the ETM application, the reason of this becomes

more understandable.

The azomethine bond (i.e. the bond between the

atoms 7 and 9 shown in Figs. 2, 5, 6 and 8) does not

affect anti-HSV-1 activity directly since both activity

features do not contain chemically bonded atoms

and all the inactivity features are not related to

that bond.

The analysis of the found features shows that the

presence of a hydrophobic part in the form of aliphatic

groups (alkanes, cycloalkanes or any their deriva-

tives) is necessary for the activity demonstration by

a molecule. The size of the cyclic groups (R1) does not

in¯uence the level of activity in view. The groups'

symmetrical arrangement relative to the nitrogen

atom is the most desirable one. Obviously, the groups

are capable of hydrophobic interaction with the corre-

sponding bio-receptor.

The other edge of the molecule can be represented

either by pyridine or chinoline ring, that can enter into

the p±p interaction with the bio-receptor. The nature

of these rings does not in¯uence substantially the

activity investigated (see Fig. 9).

Substituents in the hydrophobic and heterocyclic

parts of the molecule, that are capable of diminishing

the strength of the interactions, essentially diminish

their activity as well. It can be seen from the example

of the molecular skeleton A3, where the CH3-group in

the pyridine ring deactivates the compound.

In this way, it can be presupposed that interaction

with the receptor can occur on account of binding with

the metal±enzyme complex. The compounds can be

bonded by such tridentate ligand bonds with metal

ions. Attention has been paid to this fact earlier [21]

in the study of the anticancer activity of the given


Fig. 10. Tautomeric equilibrium characteristic of thiosemicarbazones.

Fig. 9. In¯uence of aromatic ring on the activity of compounds.

class of compounds. As the ®rst stage, the tautomer-

ization reaction takes place (see Fig. 10).

It is worth emphasizing that replacement of the

hydrogen attached to the nitrogen atom replacement

by the CH3-group (see compound 59) deactivates the

compound, because the tautomeric form II, capable of

binding with the metal±enzyme complex, is not being

formed by this. The binding can take place as shown

in Fig. 11.

In the picture we observe two ®ve-membered

helate-ring formation as a result of the interaction.

The construction of a QSAR model based on the

calculated physical±chemical characteristics and

features of activity is a matter of practical interest.

Table 4 contains the parameters used for multivariable

regression analysis. The molecules in the table are

ranked according to their activity.

If any activity feature enters a molecule provided

that any inactivity feature does not accompany it, the

molecule is considered to be active. Molecules

containing any of the inactivity features are inactive

even if they contain any activity feature.

Parameters in Table 4 are given for the inhibitory

concentration C in mg/ml; for the total energy ETOT in

a.u; for HOMO or LUMO energies EHOMO or ELUMO in

eV; for total surface area ATOT in AÊ 2.

The equation of the ®tted model is the following:

log�1=C� � 0:010ETOT 2 0:298EHOMO 2 0:329ELUMO

1 0:006ATOT 2 0:225AF1 2 0:371AF2

Here R2� 0.90, S� 0.62 (R is the correlation coef®-

cient, S is the standard error of log(1/C) estimate).

The increase in HOMO or LUMO energies results

in the decrease of the inhibitory activity, while the

increase in total energy or total surface area causes

the activity increase.

After ®nding the structural and electronic para-

meters by means of the molecular mechanics and

quantum-chemical calculations, AFi and IFi values

can be determined by examining whether the

compounds include activity or inactivity features,

and then the ®tted equation can be used to evaluate

the activity numerically.

4. Conclusions

A series of thiosemicarbazone and thiosemicarba-

zide derivatives that are potent inhibitors of HSV-1

replication has been investigated by means of an origi-

nal approach called ETM. Two features of activity

and three breaks of activity being three-dimensional

descriptions of molecular fragments responsible for

the activity/inactivity demonstration have been

revealed. In addition, a quantitative structure±activity

relationship (QSAR) model was built by means of

multivariable regression to express the activity depen-

dence on some physicochemical and structural para-

meters.

As follows from the results, in a series of

compounds with similar skeletons, a computer screen-

ing can be carried out, aiming in the series partition

into active and inactive compounds, based on the

structural features selected. The features mentioned

can be used also when planning the synthesis of

new compounds with the activity studied.


Fig. 11. A probable mechanism of interaction with the receptor.


Table 4

Some observed and calculated data related to the compounds under study (indicator I0 takes values of 1 or 0 according to the activity found

experimentally. AF1 and AF2 comprise 1 if corresponding features of activity enter a molecule, otherwise 0. IF1, IF2 and IF3 have the same

meaning for the corresponding features of inactivity. R denotes if the activity is predicted correctly (1) by the ETM)

N C ETOT EHOMO ELUMO ATOT I0 AF1 AF2 IF1 IF2 IF3 R

28 0.04 2205.76 29.59 2.46 346 1 1 1 0 0 0 1

46 0.04 2185.04 29.55 1.97 327 1 1 0 0 0 0 1

12 0.08 2139.08 29.79 1.90 246 1 1 0 0 0 0 1

6 0.10 2176.00 29.8 1.90 303 1 0 1 0 0 0 1

23 0.10 2225.61 29.68 1.90 353 1 1 0 0 0 0 1

27 0.10 2234.28 29.60 1.99 386 1 0 0 0 0 0 0

38 0.11 2170.84 29.88 2.50 306 1 0 0 0 0 0 0

58 0.12 2218.86 210.04 1.77 357 1 1 0 0 0 0 1

2 0.14 2130.37 29.83 1.88 217 1 1 0 0 0 0 1

40 0.15 2186.78 29.76 2.54 328 1 0 0 0 0 0 0

37 0.15 2202.34 29.65 1.75 332 1 1 1 0 0 0 1

51 0.17 2204.13 29.77 2.42 360 1 1 1 0 0 0 1

56 0.18 2204.16 29.29 2.53 323 1 0 0 0 0 0 0

16 0.18 2216.92 29.66 1.97 334 1 1 0 0 0 0 1

35 0.19 2233.09 29.56 1.75 377 1 1 0 0 0 0 1

57 0.20 2183.94 28.99 1.87 313 1 1 0 0 0 0 1

19 0.20 2189.89 29.65 1.96 327 1 1 1 0 0 0 1

17 0.21 2172.46 29.69 1.89 288 1 1 1 0 0 0 1

13 0.22 2181.18 29.64 1.95 341 1 1 0 0 0 0 1

47 0.22 2202.38 29.63 2.03 346 1 1 1 0 0 0 1

11 0.24 2257.05 210.06 1.66 308 1 1 0 0 0 0 1

10 0.26 2194.32 29.91 1.77 272 1 1 0 0 0 0 1

53 0.28 2174.20 29.7 3.13 303 1 0 1 0 0 0 1

20 0.28 2224.57 29.66 1.81 377 1 1 0 0 0 0 1

24 0.29 2197.12 29.63 1.92 350 1 1 1 0 0 0 1

7 0.29 2184.77 29.84 1.96 325 1 1 1 0 0 0 1

52 0.3 2156.77 29.62 3.09 270 1 1 0 0 0 0 1

49 0.32 2241.07 29.76 2.35 387 1 0 1 0 0 0 1

44 0.33 2220.04 29.79 2.48 373 1 1 1 0 0 0 1

50 0.35 2244.85 29.73 2.21 367 1 1 0 0 0 0 1

8 0.37 2179.79 29.80 1.88 294 1 1 0 0 0 0 1

45 0.37 2177.66 29.77 2.14 319 1 1 0 0 0 0 1

42 0.43 2222.52 29.78 2.51 346 0 0 0 1 1 0 1

14 0.46 2190.92 29.52 1.97 305 0 0 0 1 0 0 1

18 0.49 2188.42 29.59 1.89 308 0 0 0 1 0 0 1

60 0.56 291.40 211.08 2.77 166 0 0 0 0 0 0 0

31 0.57 2197.12 29.59 2.08 329 0 0 0 1 0 0 1

21 0.58 2154.76 29.81 1.99 287 0 1 0 0 0 1 1

25 0.60 2163.46 29.78 1.92 300 0 0 0 0 0 1 1

33 0.60 2177.78 29.79 1.66 304 0 0 0 1 1 0 1

3 0.70 2146.21 29.86 1.94 255 0 0 0 0 0 1 1

22 0.70 2147.84 29.76 1.95 263 0 1 0 0 0 0 2

15 0.85 2165.70 29.70 1.93 282 0 1 0 0 0 1 1

36 0.87 2246.87 29.65 1.53 380 0 0 0 1 1 0 1

4 1.00 2191.19 29.64 1.96 348 0 0 0 1 0 0 1

29 1.20 2154.77 29.81 1.91 284 0 0 0 1 1 1 1

1 1.20 2121.71 29.85 1.87 201 0 0 0 1 0 0 1

41 1.50 2204.08 29.76 2.53 334 0 0 0 0 1 0 1

30 1.70 2225.60 29.67 1.99 369 0 0 0 0 1 0 1

39 1.90 2212.77 29.75 2.54 381 0 0 0 0 1 0 1

The correlation equation can be used to determine

the level of the activity in view within the limits of

accuracy calculated.

References

[1] C. Shipman, S.H. Smith, J.C. Drach, D.L. Klayman, Antivir.

Res. 6 (1986) 197±222.

[2] D. Hamree, J. Bernstein, R. Donovick, Proc. Soc. Exp. Biol.

Med. 73 (1950) 275±278.

[3] D.J. Bauer, Br. J. Exp. Pathol. 36 (1955) 105±114.

[4] D.J. Bauer, Ann. N.Y. Acad. Sci. 130 (1965) 110±117.

[5] D.J. Bauer, L. St. Vincent, C.H. Kempe, A.W. Downie, Lancet

ii (1963) 494±496.

[6] D.J. Bauer, L. St. Vincent, C.H. Kempe, P.A. Young,

A.W. Downie, Am. J. Epidemiol. 90 (1969) 130±145.

[7] G.G. Heiner, N. Fatima, P.K. Russell, A.T. Haase, N. Ahmad,

N. Mohammed, D.B. Thomas, T.M. Mack, M.M. Khan,

G.L. Knatterud, R.L. Anthony, F.R. McCrumb Jr., Am. J

Epidemiol. 94 (1971) 435±449.

[8] D.C. Hut®eld, G.W. Csonka, Lancet i (1964) 329±330.

[9] R.W. Sidwell, G. Arnett, G.J. Dixon, F.M. Schabel, Proc. Soc.

Exp. Biol. Med. 131 (1969) 1223±1230.

[10] R.W. Brockman, R.W. Sidwell, G. Arnett, S. Shaddix, Proc.

Soc. Exp. Biol. Med. 133 (1970) 609±614.

[11] C. Hansch, A. Leo, Substituent Constants for Correlation

Analysis in Chemistry and Biology, Wiley-Interscience,

New York, 1979.

[12] T. Fujita, in: G. Jolles, K.R.H. Wooldrige (Eds.), The Role of

QSAR in Drug Design, Academic Press, London, 1984,

pp. 45±79.

[13] N.M. Shvets, A.S. Dimoglo, Nahrung 42 (6) (1998) 364±370.

[14] A.S. Dimoglo, N.M. Shvets, Flavour perception aroma

evaluation, in: H.-P. Kruse, M. Rothe, (Eds.), Proceedings

of the 5th Wartburg Aroma Symposium, Eisenach, 1997,

pp. 161±170.

[15] A.S. Dimoglo, Y.M. Chumakow, B.N. Dobrova, M. SaracËo-

glu, Drug Res. 47(4) (1997) 415±419.

[16] N. Shvets, Comput. J. Moldova 1 (3) (1993) 101±110.

[17] N. Shvets, Comput. J. Moldova 5 (3) (1997) 309±321.

[18] K.B. Wiberg, Tetrahedron 24 (1968) 1083.

[19] N. Shvets, A. Terletskaya, A. Dimoglo, Y. Chumakov, Theor.

Chem. 463 (1999) 105±110.

[20] A. Terletskaya, N. Shvets, A. Dimoglo, Y. Chumakov, Theor.

Chem. 463 (1999) 99±103.

[21] K. Gilbert, J. Gaevski, A MMPMi Molecular Mechanics

Program, Indiana University, Indiana, 1985.

[22] J.A. Pople, G.A. Segal, J. Chem. Phys. 44 (9) (1966) 3289±

3296.

[23] J.A. Pople, D.P. Santry, G.A. Segal, J. Chem. Phys. 43 (10)

(1972) 129±135.


Table 4 (continued)

N C ETOT EHOMO ELUMO ATOT I0 AF1 AF2 IF1 IF2 IF3 R

26 2.00 2163.49 29.84 2.09 290 0 0 0 0 0 1 1

43 2.20 2248.50 29.77 2.48 367 0 0 0 1 0 0 1

34 2.40 2221.15 29.58 2.20 389 0 1 0 0 1 0 1

59 3.20 2147.82 29.69 2.74 263 0 0 0 0 0 0 1

54 3.90 2129.30 211.14 2.48 186 0 0 0 0 0 0 1

9 4.60 2204.38 29.60 1.94 337 0 0 0 1 0 0 1

5 5.40 2274.73 29.70 2.02 368 0 0 0 0 0 1 1

32 5.60 2169.16 29.82 1.68 292 0 0 0 0 1 0 1

55 7.00 2164.53 29.19 1.43 274 0 0 0 1 0 0 1

48 8.50 2207.71 29.95 2.35 360 0 0 0 0 0 0 1

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