Topological Substituent Descriptors

 

 

Mircea V. DIUDEA1*, Lorentz JÄNTSCHI2, Ljupčo PEJOV3

 

1“Babeş-Bolyai” University Cluj-Napoca, Romania

2 Technical University Cluj-Napoca, Romania

3“Sv. Kiril i Metodij” University Skopje, Macedonia

*corresponding author, diudea@chem.ubbcluj.ro

 

 

Abstract

Motivation. Substituted 1,3,5-triazines are known as useful herbicidal substances. In view of reducing the cost of biological screening, computational methods are carried out for evaluating the biological activity of organic compounds. Often a class of bioactives differs only in the substituent attached to a basic skeleton. In such cases substituent descriptors will give the same prospecting results as in case of using the whole molecule description, but with significantly reduced computational time. Such descriptors are useful in describing steric effects involved in chemical reactions.

Method. Molecular topology is the method used for substituent description and multi linear regression analysis as a statistical tool.

Results. Novel topological descriptors, XLDS and Ws, based on the layer matrix of distance sums and walks in molecular graphs, respectively, are proposed for describing the topology of substituents linked on a chemical skeleton. They are tested for modeling the esterification reaction in the class of benzoic acids and herbicidal activity of 2-difluoromethylthio-4,6-bis(monoalkylamino)-1,3,5-triazines.

Conclusions.  Ws substituent descriptor, based on walks in graph, satisfactorily describes the steric effect of alkyl substituents behaving in esterification reaction, with good correlations to the Taft and Charton steric parameters, respectively. Modeling the herbicidal activity of the seo of 1,3,5-triazines exceeded the models reported in literature, so far.

 

Keywords

Steric effect, Substituent descriptors, Molecular topology, Herbicidal activity.

Abbreviations and notations

MLR, multi linear regression; SVTI, substituent volume topological index; Es, Taft’s steric parameter; n, Charton’s steric parameter.

 

 

1. Introduction

In the field of chemical reactivity, the first proposal of a substituent steric parameter is due to Taft [1, 2]. He tried to quantify the steric influence of a substituent located on the hydrocarbon part of organic esters in the acid-catalysed hydrolysis of aliphatic carboxylic esters, RCOOR’. His Es steric parameter is defined as:

 

                                                                                     (1)

 

where  is the ratio of acid-catalysed hydrolysis rate constant of RCOOR’ to that of  MeCOOR’. By definition, .

            The Es parameter has been defined empirically [3].  Taft himself pointed out that Es varies parallel to the atom group radius. Charton also found that Es is linearly dependent on the van der Waals radius of the substituent, thus defining a new steric parameter, n [4-8].

            Murray [9] found correlations between the Taft parameter and the Randić [10] topological index, for a series of substituted alkyls. In this respect, Ivanciuc and Balaban [3] have proposed a topological descriptor, SVTI, which encodes the topological distances (i.e., the number of bonds/edges, Dij, joining the atoms/vertices i and j on the shortest path) in a molecular graph, G.

It is defined on the fragment F (i.e. an alkyl group) attached to the vertex i of G, as:

 

                                                                                                    (2)

 

The summation runs over all NF vertices of F and the distance Dij is limited to 3, in agreement to the Charton’s conclusion about the limit of the influence of the steric effect beyond the gamma carbon [5-8].

The calculation of SVTI is exemplified for the sec-butyl group (R = H) or higher homologues (R ¹ H):

SVTI (s-Bu) = 1+ 2 + 2 + 3 = 8

 

The above authors have tested their descriptors in describing the reaction rates of acid-catalysed hydrolysis of RCOOR' (the Taft's set).

            In the present work, two novel descriptors for substituents are proposed. They are now tested in modeling the effector-receptor interaction in the herbicidal activity of 2-difluoromethylthio-4,6-bis(monoalkylamino)-1,3,5-triazines.

 

 

2. Substituent Topological Descriptors,   XLDS  and   Ws

 

            The substituent descriptors XLDS and Ws herein proposed are constructed with the aid of layer matrices.

Before defining our descriptors, let’s recall some knowledges about the layer matrices [11-17].

            A partition G(i) with respect to the vertex i, in a graph, is defined [11, 14, 15]  as:

 

                                                                              (3)

 

where Diu is the topological distance (see above) and  ecci is the eccentricity of i (i.e. the largest distance between i and any vertex in G). Figure 1 illustrates the relative partitions for the graph G1.

Let  be the layer j of the vertices u located at distance j, in the relative partition G(i):

 

                                                                                                                (4)

 

The entries in a layer matrix, LM, collect the topological property Pu  for all vertices u belonging to the layer :

 

                                                                                                                     (5)

G1

G1(1,5)

G1(2)

G1(3)

G1(4)

G1(1) = {{1},{2},{3,5},{4}};             G1(2) = {{2},{1,3,5},{4}};

G1(3) = {{3},{2,4},{1,5}};                 G1(4) = {{4},{3},{2},{1,5}};

G1(5) = {{5},{2},{1,3},{4}}.

Figure 1. Partitions of G1 with respect to each of its vertices

           

            The matrix LM can be written as:

 

                        LM(G) =                                                     (6)

 

where V(G) is the set of vertices in graph and d(G) is the diameter (i.e., the largest distance) of G. The dimensions of such a matrix are N´ (d(G)+1).

Figure 2 illustrates the layer matrix of distance sums, LDS [13], the topological property M which collects being the sum of distances joining a vertex i with all the remainder vertices in G. Note that the first column contains just the vertex topological property.

(in this case, , marked in the weighted graph, G2{DSi}).

 

 

G2

               i \ j:  0      1       2       3      4      

               (1)   15    10     24     26    17

               (2)   10    39     26     17      0

               (3)     9    36     47       0      0

               (4)   12    26     24      30     0

               (5)   17    12       9      24   30

               (6)   15    10     24      26   17

               (7)   14      9     22      47     0

LDS(G2)

Figure 2. Matrix LDS for the graph G2

 

This matrix and the invariants calculated on (e.g., the well-known Wiener index [18], counting all distances in G) are useful tools in topological description of molecular graphs [13, 14].

            Another interesting matrix is the layer matrix of walk degrees [15], LeW.  A walk, W, is defined [19] as a continuous sequence of vertices, v1, v2, ..., vm; it is allowed edges and vertices to be revisited.

If the two terminal vertices coincide (v1 = vm ), the walk is called a closed (or self returning) walk, otherwise it is an open walk.

If its vertices are distinct, the walk is called a path. The number e of edges traversed is called the length of walk.

Walks of  length e, starting at the vertex i, eW(i), can be counted by summing the entries in the row i of the eth  power of the adjacency matrix A (whose nondiagonal entries are 1 if two atoms are adjacent and zero otherwise):

 

                                                                                                                  (7)

 

where eW(i)  is called the walk degree (of rank e) of vertex i (or atomic walk count [15, 20] ).

Walk degrees, eW(i), can be also calculated by summing the first neighbours degrees of lower rank, according to an additive algorithm11 illustrated in figures 3 and 4.

Local and global invariants based on walks in graph were considered for correlating with physico-chemical properties [15, 20].

Figure 3 illustrates the layer matrix of walk degrees, LeW,  e = 1-4, for G2. Note that the first column in L1W is just the vertex degree or the vertex valency. Note that the matrix LeW was re-invented by Randic in 2001, for e = 1, under the name “valence shells” [21].

The substituent descriptor XLDS is the local “centrocomplexity index”, XLM [14], defined on the LDS matrix:

 

                                                                                                    (8)

 

where i is the attachment point of the substituent to a given chemical structure (see figure 4) and  z denotes the number of bits of  max[LDS]ij in G. Calculation of XLDS is exemplified in figure 4.

 

G2 {1Wi }                     G2 {2W}                    G2 {3Wi }                     G2 {4Wi }

L1W

L2W

L3W

L4W

i \ j       0   1   2   3   4           0   1   2    3    4          0    1    2    3   4           0    1    2    3    4

     1      1   3   4   3   1          3    5   9    7    2          5  12  17  14   4         12  22  38  28    8

    2      3   5   3   1   0          5  12   7    2    0        12  22  14    4   0         22  50  28    8    0

    3      3   6   3   0   0          6  12   8    0    0        12  26  14    0   0         26  50  32    0    0

    4      2   4   4   2   0          4    8   8    6    2          8  16  18  10   0         16  34  34  24    0

    5      1   2   3   4   2          2    4   6    8    6          4    8  12  18 10           8  16  26  34  24

    6      1   3   4   3   1          3    5   9    7    2          5  12  17  14   4         12  22  38  28    8

    7      1   3   5   3   0          3    6   9    8    0          6  12  20  14   0         12  26  38  32    0

 

Figure 3. Layer matrix of walk degrees, LeW for the graph G2

(calculated by summing the first neighbor degrees of lower rank)

 

 

{1W}

{2W}

{3W}

{DS}

(a)    

      Ws (i) = 7+7/2+11/3+8/4 ≈ 16.167;

     XLDS(i) = 14∙100+10∙10-2+8∙10-4+8∙10-6+12∙10-8+12∙10-10 = 14.1008081212 ≈ 14.101;  (b)

Figure 4. (a) Walk degrees, eW, (calculated by summing the first neighbors degrees

of lower rank) and distance sums, DS;  (b) Evaluation of Ws and XLDS descriptors

 

 

Ws is based on the walks in a connected molecular graph.  It is calculated from the layer matrix L3W by:

                                                                                                      (9)

 

where 3W is the walk number, of length 3.

We limited here to elongation 3 by following  the Charton’s suggestion about the limit of the influence of steric effect (see above). The calculation of the parameter Ws is exemplified in figure 4.

            The XLDS  descriptor is similar to the SVTI parameter, both of them counting distances in the substituent.

Ws describes the branching in the vicinity of the attachment point i.

All these parameters suggest the steric influence of a substituent in the interaction of the skeleton (or a situs of it) with a partner (e.g., a reactant [3, 22] or a biological receptor). They are free of electronic contributions, at least in the variant in which the heteroatom is not considered.

 

 

3. Correlating Test

 

            The utility of the substituent descriptors, XLDS and Ws, was proven on a set of thirty aminoalkyl fragments (table 1) involved in the inhibition of Hill reaction of triazines [23] (figure 5).

In this respect, the fragmental volumes, V, (in cm3/mol) for the considered substituents have been calculated as described below. Other parameter herein considered was the number of atoms different from hydrogen, N.

All these descriptors have been calculated separately for the two sites, A and B (see figure 5).

 

Figure 5. Herbicidal bioactive triazines


Table 1. Topological descriptors and biological activity pI50 for the triazines in figure 5

No

A

B

NA

NB

Ws, A

Ws, B

XA *

XB

VA **

VB

pI50

1

NH2

NH2

1

1

1

1

1.1

1.1

18.763

18.763

3.82

2

NH2

NHCH3

1

2

1

5

1.1

3.23

18.763

32.636

5.20

3

NH2

NHC2H5

1

3

1

8.5

1.1

6.446

18.763

47.908

5.34

4

NH2

NH-i-C3H7

1

4

1

13.66

1.1

9.061

18.763

60.766

5.83

5

NHCH3

NHCH3

2

2

5

5

3.23

3.23

32.636

32.636

6.01

6

NHCH3

NHC2H5

2

3

5

8.5

3.23

6.446

32.636

47.908

6.39

7

NHCH3

NHC3H7

2

4

5

11.75

3.23

10.071

32.636

62.393

6.75

8

NHCH3

NH-i-C3H7

2

4

5

13.66

3.23

9.061

32.636

60.766

6.76

9

NHCH3

NHC4H9

2

5

5

13.93

3.23

15.111

32.636

76.638

6.74

10

NHCH3

NH-s-C4H9

2

5

5

15.16

3.23

13.091

32.636

75.039

6.76

11

NHCH3

NH-t-C4H9

2

5

5

20.50

3.23

12.081

32.636

74.106

6.78

12

NHCH3

NHC5H11

2

6

5

15.62

3.23

21.161

32.636

88.241

7.12

13

NHC2H5

NHC2H5

3

3

8.5

8.5

6.446

6.446

47.908

47.908

6.82

14

NHC2H5

NHC3H7

3

4

8.5

11.75

6.446

10.071

47.908

62.393

6.74

15

NHC2H5

NH-i-C3H7

3

4

8.5

13.66

6.446

9.061

47.908

60.766

6.89

16

NHC2H5

NHC4H9

3

5

8.5

13.93

6.446

15.111

47.908

76.638

6.95

17

NHC2H5

NH-i-C4H9

3

5

8.5

16.16

6.446

14.101

47.908

74.497

7.01

18

NHC2H5

NH-s-C4H9

3

5

8.5

15.16

6.446

13.091

47.908

75.039

6.87

19

NHC2H5

NH-t-C4H9

3

5

8.5

20.50

6.446

12.081

47.908

74.106

6.97

20

NHC2H5

NHC5H11

3

6

8.5

15.62

6.446

21.161

47.908

88.241

6.94

21

NHC2H5

NHC6H13

3

7

8.5

17.00

6.446

28.222

47.908

102.032

7.21

22

NHC2H5

NHC7H15

3

8

8.5

18.17

6.446

36.292

47.908

116.672

7.01

23

NHC2H5

NHC8H17

3

9

8.5

19.18

6.446

45.373

47.908

128.770

6.81

24

NHC3H7

NHC3H7

4

4

11.75

11.75

10.071

10.071

62.393

62.393

6.45

25

NH-i-C3H7

NHC3H7

4

4

13.66

11.75

9.061

10.071

60.766

62.393

6.75

26

NH-i-C3H7

NH-i-C3H7

4

4

13.66

13.66

9.061

9.061

60.766

60.766

6.75

27

NH-i-C3H7

NHC4H9

4

5

13.66

13.93

9.061

15.111

60.766

76.638

6.71

28

NH-i-C3H7

NH-s-C4H9

4

5

13.66

15.16

9.061

13.091

60.766

75.039

6.88

29

NH-i-C3H7

NH-t-C4H9

4

5

13.66

20.50

9.061

12.081

60.766

74.106

6.70

30

NH-i-C3H7

NHC5H11

4

6

13.66

15.62

9.061

21.161

60.766

88.241

6.69

 

* The symbol X stands for XLDS (see text);

** Volume, [cm3/mol].

 

Table 2. Statistics of multivariable regression (distinct variables on branches A and B)

No.

XI

bi

A

r

s

v(%)

F

1

1/N,B

-3.786

7.549

0.8987

0.311

4.752

117.587

2

1/Ws,B

-3.372

6.933

0.8298

0.396

6.047

61.899

3

1/XB

-3.806

7.038

0.8835

0.333

5.076

99.598

4

1/VB

-72.276

7.760

0.8975

0.313

4.779

115.936

5

1/NA

1/NB

-1.234

-2.678

7.810

0.9577

0.208

3.175

149.557

6

1/Ws,A

1/Ws,B

-1.335

-2.077

7.118

0.9615

0.199

3.030

165.554

7

1/XA

1/XB

-1.317

-2.526

7.252

0.9662

0.186

2.844

189.755

8

1/VA

1/VB

-22.999

-52.514

8.048

0.9478

0.231

3.519

119.237

9

1/Ws,A

1/VB

-1.114

-47.194

7.618

0.9714

0.172

2.619

226.162

10

1/Ws,A

1/XB

-1.180

-2.458

7.159

0.9729

0.167

2.550

239.280

11

1/Ws,A

1/NB

-1.120

-2.484

7.484

0.9746

0.162

2.472

255.491

12

NA

1/NA

1/NB

-0.385

-2.777

-2.444

9.477

0.9834

0.134

2.039

254.937

13

Ws,A

1/Ws,A

1/Ws,B

-0.025

-1.594

-2.056

7.372

0.9661

0.190

2.903

121.327

14

XA

1/XA

1/XB

-0.078

-2.047

-2.413

7.876

0.9818

0.140

2.132

232.401

15

VA

1/VA

1/VB

-0.036

-65.998

-45.367

10.649

0.9808

0.144

2.193

219.227

16

XA

1/VA

1/VB

-0.155

-59.011

-46.244

9.762

0.9815

0.141

2.152

228.039

17

XA

1/VA

1/XB

-0.154

-60.818

-2.399

9.337

0.9836

0.133

2.029

257.426

18

XA

1/VA

1/NB

-0.153

-58.888

-2.430

9.614

0.9846

 

0.129

1.968

274.318

In table 2 A and bi values are the coefficients of:

 

                                                                                         (10)

 

and leave one out procedure (loo) has the results:

 

loo(12): r = 0.9768; s = 0.153; v(%) = 2.332;

loo(18): r = 0.9778; s = 0.149; v(%) = 2.271.                                                  (11)

 

The inhibitory activities of triazines on Chlorella have been taken from the study of Morita et al [24]. They are expressed as pI50, which represents the negative logarithm of concentration required for 50% inhibition of Hill reaction. The correlating results are listed in table 2.

 

 

4. Results and Discussion

 

In single variable regression, the descriptors for the substituents in branch B (table 2) are not satisfactory to model the inhibitory activity of triazines; the correlation coefficient, r, is lower than 0.9 (for those in A, r is still lower) and the coefficient of variance, v, is about 5 %. Note that all these "steric" descriptors are taken as reciprocal values, suggesting that the triazine ring fits at the biological receptor as better as the substituent is less sterically involved.  

            In two variables regression, by adding the descriptors for the branch A the correlation is improved, as indicates the higher values for r and F (the Fisher ratio) and the drop in the dispersion, s, and v(%) values (entries 5-8, table 2). When the descriptors for the two branches are heterogeneous, the result is still better (entries 9-11).

            In three variables regression, the correlation is once more improved. Again the heterogeneous descriptors model the inhibition reaction better that the homogeneous ones (compare entries 16-18 with 12-15, Table 2).

The best model found (see also entry 18) was:

 

                        pI50 = 9.614 – 0.153∙XA – 58.888∙1/VA – 2.430∙1/NB;

                        n = 30; r2 = 0.9694; s = 0.129; v(%) = 1.968; F = 274.3;                                (10)

The cross validation (leave-one-out, “loo”, procedure) test for the equations in entries 12 and 18 are given in the bottom of table 2.

            Despite the excellent model offered by equation (10), a brief inspection on the general structure of these triazines showed a rather surprising error: the molecule is symmetric, so that the two branches A and B are interchangeable! In consequence, the two columns of descriptors have no meaning if they are taken as distinct descriptors. Thus, the contribution of the substituents in A and B in modeling the global biological activity must somehow be mixed!   

The simple summation (or simple arithmetic mean) of contributions of the two branches, A and B, did not provide satisfactory results. More reliable appeared in other kinds of average: geometric (“geo”) and harmonic (“har”). The best correlating results are included in table 3. The cross validation test, loo, is given for each entry.

From table 3 it appears that, in single variable regression, the descriptor 1/X(LDS)geo  provides a rather good (r> 0.95) description of the activity, both in estimation and prediction, "loo" (entry 2).

The best prediction is offered by the three variables equation, in entry 6 (r >0.975), all of them as harmonic average of the descriptors of A and B branches:

 

pI50 = 10.292 – 119.503∙1/Vhar – 0.097∙Xhar – 0.047∙Ws,har;

n = 30; r = 0.9807; s = 0.144; v(%) = 2.198; F = 218.158;                              (11)

 

The corresponding arithmetic averaged descriptors used in (11) supplied a correlation of r = 0.955 which is, of course, unsatisfactory.

This equation was chosen for a tempting prediction in the past.  The experimental data for the compounds no. 3, 12, 21 and 24 (showing residuals, ycalc-yexp, about two times or larger than the value of standard error of estimate:

s = +0.144; -0.254; +0.236; +0.301 and -0.398, respectively

were changed by the values:

5.6209; 6.8778; 6.9073 and 6.8471, respectively

calculated by equations:

 

pI50 = 10.292 – 119.503 1/Vhar – 0.097 Xhar – 0.047 Ws,har

n = 26;  r = 0.9932; s = 0.086; v(%) = 1.309; F = 530.484                              (12)

The correlating data, obtained by using the new column of activities, ycor, are included in table 3 as the rows "ycor". The improvement in the statistical parameters of the regression equations is obvious for all data of table 3 (where * means "leave one out" cross validation procedure; and ** are yi corrected for i = 3, 12, 21 and 24):

 

Table 3. Statistics of multivariable regression, Ycalc = a + åi biXi (averaged variables)

No.

Xi

bi

a

r

s

v(%)

F

1

1/Ws,har

loo*

ycor**

-3.151

 

7.121

 

0.9553

0.9467

0.9666

0.210

0.229

0.175

3.204

3.489

2.669

292.215

 

398.721

2

1/Xgeo

loo

ycor

-3.891

 

 

7.253

 

 

0.9621

0.9558

0.9793

0.194

0.209

0.138

2.956

3.183

2.108

348.097

 

655.776

3

1/Vhar

Nhar

loo

ycor

-126.800

-0.541

 

 

11.091

0.9763

 

0.9721

0.9924

0.156

 

0.167

0.086

2.387

 

2.543

1.307

275.063

 

 

875.466

4

1/Vhar

Xhar

loo

ycor

-113.340

-0.137

10.010

0.9777

 

0.9735

0.9907

0.152

 

0.163

0.095

2.318

 

2.480

1.446

292.278

 

 

713.286

5

1/Nhar

Xhar

Ws,har

loo

ycor

-5.614

-0.056

-0.057

9.491

0.9798

 

 

0.9742

0.9918

0.147

 

 

0.160

0.091

2.247

 

 

2.446

1.380

208.342

 

 

 

523.336

6

1/Vhar

Xhar

Ws,har

loo

ycor

-119.503

-0.097

-0.047

10.292

0.9807

 

 

0.9752

0.9938

0.144

 

 

0.157

0.079

2.198

 

 

2.397

1.204

218.158

 

 

 

690.328

7

1/Vhar

Xhar

Ws,har

Nhar

loo

ycor

-105.131

-0.232

-0.081

0.673

9.058

0.9824

 

 

 

0.9742

0.9938

0.141

 

 

 

0.160

0.080

2.144

 

 

 

2.444

1.226

172.608

 

 

 

 

499.253

8

1/Nhar

Xhar

Ws,har

Vhar

loo

ycor

-4.724

-0.228

-0.070

0.052

7.858

0.9825

 

 

 

0.9751

0.9924

0.140

 

 

 

0.157

0.089

2.139

 

 

 

2.401

1.358

173.400

 

 

 

 

405.773

 

 

More over, among the 24 descriptors (N, V, Ws, XLDS , 1/N, 1/V, 1/Ws,  1/XLDS, taken as "ari", "har" and "geo" average) used in single variable regression, in 20 of them an improvement of the statistics was recorded. Again the equation in entry 6 was the best model. This test suggested that the experimental data for the compounds, above mentioned, are "in error".

From eq 11 and table 3, it comes out that the inhibitory activity of triazines is controlled by the possibility of the triazine ring (i.e., the pharmacophor) to accommodate at the receptor situs.

This opinion is supported by the reciprocal values and the negative regression coefficient, and negative partial correlation index of these "steric" descriptors involved in an eq. of type 11.  It suggests that the triazine ring fits at the biological receptor as better as the substituent is less sterically involved.

A plot of the observed vs. calculated (by eq 11) pI50 values is given in figure 6. For comparison, the plot for the same descriptors and “ycor” is given in Figure 7.

 

 

Figure 6. Plot of experimental biological activity (VAR1) vs. ycalc. (cf. eq 11) values

 

 

 

 

Figure 7. Plot of experimental biological activity (VAR1) vs. ycor values

 

 

4. Computation of Fragmental Volumes

 

            The geometries of the hydrocarbon fragments (in fact, the corresponding radicals) were fully optimized at the Unrestricted Hartree-Fock (UHF) level of theory, using the 6-31G** basis set (of DZP quality), which contains a single set of d polarization functions on carbons, and a single set of p polarization functions on hydrogens for better description of the radical wavefunctions.

The Berny's optimization algorithm was used (the energy derivatives with respect to nuclear coordinates were computed analytically [25]), along with the initial guess of the second derivative matrix.

Standard harmonic vibrational analysis was applied to test the character of the optimized geometries (stationary points at the potential energy hypersurfaces - PES). All stationary points corresponded to real minima on the explored PES.

Molecular volume calculations were performed for the optimized structures, by the Monte-Carlo method. Since Monte-Carlo method for calculating molecular volume (defined as the volume inside a contour of 0.001 electrons/Bohr3 density) is stochastically based algorithm, it often leads to results accurate up to several percents.

Therefore, 11 volume calculations per fragment were performed for each fragment, and the arithmetic average value was taken as the closest approximation to the real one (at the level of theory employed).

In order to increase the density of points for a more accurate integration, the "Tight" option of the Gaussian "Volume" keyword was used. All calculations were performed with Gaussian 94 suite of programs [26].

 

 

5. Conclusions

 

The Ws descriptor, based on the walks in graph, satisfactorily describes the steric effect of alkyl substituents in the esterification reaction.

It is a pure steric parameter, not affected by the electronic effects. Ws correlate well to the fragmental volumes (over 0.92) and show a lower degeneracy in comparison to the SVTI, n and Nc parameters.

It is also well correlated18 to the Taft, Es, (0.9637), and Charton, n, (0.9587), parameters, which makes from Ws a promising alternative in describing the steric effect of alkyl substituents.

 

 

6. Acknowledgment

 

The work was supported in part by the Romanian GRANT CNCSIS 2002.

 

 

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