Embed Size (px)
Citation preview
Indian Journal of C hemi stry Vol. 45A, January 2006. pp. 194-20 1
Comparative 2D and 3D-QSAR of antifungal griseofulvin analogues
Dhanshri C Juvale, Shivajirao S K adam & Vithal M Kulkarni*
Poona College of Pharmacy, Bharati Vidyapeeth Deemed U ni vers ity, Erand wane, Pune 41 1 038, India
Ema il: [email protected]
Received 21 February 2005; revised 15 November 2005
A seri es of gri seoful vin ana logues has been examined to dete rmifle the structura l req ui rements of antifungal acti vity by three-dimensional quanti tative structure-activity re lationship studies using comparative molecular field analys is (CoM FA) and comparative mol ecular simil arity indices ana lysis (CoMS IA ) methods. Evaluation of 22 compounds (train ing set) has estab li shed the CoMFA and CoMS[A mode ls, w hi ch have been va lidated by evaluati on of a set of four compounds (test set). The lowest energy conformer of the most act ive molecule obta ined fro m systematic search has been used as the template struc ture for the a li gnment. [n thi s study, the superim position of mo lec ules was carried o ut by atom-based fit (rms), multi f it and fi e ld fit. Along with ste ri c and e lectronic fi e lds, Clog P has been taken as add itional desc ri ptor to account for lipophilic ity. Further, a QSAR analys is has bee n carried out us ing Tools fo r Structure-Acti vity Relationship software. For thi s study, various chemical descriptors such as e lectronic, steric, lipophilic, topologica l have been calc ul ated and mult iple regression carried out. T he QSAR models from these three methods ex hibit considerab le correlative and predicti ve properties. The mode ls obtained from the preselll study may be useful fo r the development of new griseofulvin ana logues as pote ntial antifungals.
Griseoful vin is a sys te mic fungicidal for treating superfaci al mycoses iso lated from Strptomyces. It inhibits fungal mitosis by disruption of mitotic spindle l
. A number of g ri seofulvin analogues have been synthes ized"-4. Classical Quantitative structureactivity relationship (QSAR) for a series of gri seoful vin analogues was reported in 197 1 by Hansch & Lein5
. The correlation was not so sharp since ideal substituent constant and steric parameter were not available. Such studies are classified as 2D QSAR as the descriptors do, not contain any info rmation regarding conformatio n of ligand. Hansch & Lein reported th at the activity of gri seofulvin ana logues depends linearly on electronic effect and lipophilicity
of substituents, a, to the carbonyl function.
In order to compare and validate the results, we have applied three-dimensio na l quanti tati ve structureactivity re lationship (3D-QSAR) usi ng both comparative molecular fi e ld anal ysis (CoM FAt and comparative molecular similarity indi ces analysis (CoMSIAf methods. Further, we have carri ed out a QSAR study using Too ls fo r Structure-Activ ity Relationship 3 .3 (TSAR)8-IO for the same seri es.
The 3D- QSAR, CoM FA method was proposed by Cramer el a / .
6 in 1988, which is extensive ly used in the practi ce of drug di scovery . One of the advantages
of CoMFA is the a bility to produce conto ur maps as a result of QSAR analysis between steri c and electron ic properties and bio logical activities . CoMFA has been wide ly applied in drug desig n 11 - 15 . The CoMSIA
method of 3D-QSAR was introduced by Kle be7
in1 994, in which a common probe ato m a nd simil arity indices are calculated at regul arly spaced grid po ints for prealigned molecules. The CoMSIA 14.15 technique
is of particular interest si nce it considers hydrophobic field, hydrogen bond donor and acceptor fields in additi on to electronic and steric fields and is less alignme nt-sensiti ve than CoMFA .
Methodology Biological data
Twenty-eight mo lecules selected for the present s tudy were taken from the published work by Crosse et al. 16 The structure of the compounds and their bi o logical data are g iven in Tabl e 1. The biological activity is expressed in terms of ratio of griseofulvin concentration to analogue concentrati on required fo r 'curling' of hyphae test in Borrytis allii. The logarithm of relative antifungal activ ity to griseoful vi n (molar response, MR) was used as bi o log ical activ ity in 3D-QS AR studies, thus corre lating the data linear to the free energy change. For CoMFA and Co MSIA analys is, a training set of twenty-two
JUVALE el al. : QSAR STUDIES OF ANTIFUNGAL GRTSEOFULVIN ANALOGUES 195
molecules (1 -22, Table 1) was used for generation of QSAR models. The training set molecules were selected in such a way that there was no redundancy in information content in terms of both structural features and activity ranges. The most active compounds were included so that they provide critical information on pharmacophore requirements. Several moderately active and inactive compounds were also included to spread the activity ranges . Predictive power of the resul ting models was evaluated using a test set of four molecules (23-26, Table 1). Selection
of test set molecule was made by considering the fact that, test molecu les represent a range of biological activity simi lar to training set.
Computational details 3D-QSAR using CoMFA and CoMSIA
All computational studies were performed using SYBYL 6.9.11 7 with standard Tripos force field l 8
. The compounds were constructed from the fragments in the SYBYL database with standard bond lengths and bond angles and geometry optimization was carried
Table I - Structures and activ ities of the molecu les in the training (1-22) and tes t (23-26) sets
0
RO
CI
Compd R RI R2 X Rat io found in BOlrVl is allii Antifungal activity. test for "curling" of hyphae" log MR Ob Sb
Me Allyl Me H 0.03
2 n-Pr Me Me H 0.03
3 Me Me Me Me I 0.01
4 Me Me Me N-Pr 2 0.35
5 Me Me Me All yl 2 0.35
6 Me Me Me Br 4 0.69
7 Me Me Mc I 8 1.03
8 Me Me Et H 2 0.31
9 Me Me II -PI' H 4 0.63
10 Me Me Ally l H 4 0.63
11 Me Me Ph H 4 067
12 Me Me PhCh2 H 8 0.99
13 Me Me Et CI 4 0.66
14 Me Me Et Br 4 0.70
15 Me Me Et I 16 1.39
16 Me Me Et PhCH 2 16 1.31 "
17 Me Me Il -Pr Br 64 1.92
18 Me Me II-P I' Il -Pr 8 0.98
19 Me PhCH 2 Me H 2 0.38
20 Me Me Me PhCH} 2 0.40
21 Me Me Il-Pr I 512 2.86
22 Me Me II-PI' PhCH 2 512 2.83
23 Allyl Me Me H I 0.03
24 Me Me Me CI 4 n.M 25 Me Me Il-B u H 8 0.95
26 Me Me Et Et 2 0.35 27' Et Me Me H 8 0.92 28' Me Me Il-Bu PhCl-l 2 8 0.14
" Ratio of concentrati on of gri seo fu lvin to analogue concentrat ion requireci for the curli ng hyphae tes t l 6
h Logarithm of relative ant ifungal acti vity to gri seofulv in exprc!sseci as molar response ( log MR)' . C Compounds not considered for development of QSAR model.
196 INDlAN J CHEM, SEC A, JANUARY 2006
out using the standard Tripos forcefield with distance dependent-dielectric function and energy gradient of 0.001 kcallmol A. The initial conformations were obtained from systematic search. The lowest energy conformers were selected and minimized by using Powell method till root-mean-square (rms) deviation of 0.001 kcal/mol A was achieved. Partial atomic charges required for calculation of the electrostatic interaction were computed by semiempirical molecular orbital method using AMI in MOPAC program. Alignment of the compounds is a very important feature of CoMFA analysis. The most active griseofulvin analogue (molecule 21) was used as template for aligning the other analogues.
Alignment rules
In the present study, we have superimposed all the compounds on compound 21 by three alignment rules .
Alignment 1: This was done by atom-based fitting of the atoms to the most active molecule, compound 21. The ring atoms of the molecules were used for rms fitting, as shown in Fig. 1. The superimposition of all the molecules is shown in Fig. 2.
MeO
Fig. I-Molecule 21 with atoms used for superimposition marked in blue.
Fig. 2-Superimposition of molecu les (training and test set) using RMS fittin g.
Alignment 2: In this case, alignment of the molecules was carried out by flexible fitting (mu]tifit) of atoms, of the molecules to the template molecule, compound 21. This involved energy calculations and fitting onto the template molecule by applying force (force constant 20 kcallmol) and subsequent energy minimization.
Alignment 3: This was carried out using the SYBYL QSAR rigid body field fit command within SYBYL and using compound 21 as template molecule. Field fit adjusts the geometry of the molecules such that its steric and electrostatic fields match the fields of the template molecule.
Generation of CoMF A and CoMSIA fields For each alignment, the steric and electrostatic
potential fields for CoMF A were calculated at each lattice intersection of a regularly spaced grid of 2.0 A in all X, Y and Z directions. The van der Waals potential (Lennard-Jones, 6-12) and columbic term, which represent, respectively, steric and electrostatic fields, were calculated using the Tripos force field. A distance-dependent dielectric constant of 1.0 was used. An Sp3 carbon atom with van der Waals radius of 1.52A and + 1.0 charge was served as the probe atom to calculate steric and electrostatic fields . The steric and electrostatic contributions were truncated to ±30 kcallmol, and the electrostatic contributions were ignored at lattice intersections with maximum steric interactions.
The five CoMSIA similarity index fields available within Sybyl (steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor) were calculated at lattice points using a common probe atom of 1A radius , as well as the charge, hydrophobicity and hydrogen bond properties of 1 and an attenuation factor of 0.3.
Partial Least Square (PLS) analysis PLS 19-20 was used in conjugation with the cross
validation (leave-one-out) option to determine the optimum number of components, which were then used in deriving the final 3D-QSAR model without cross-validation. The results from cross-validation analysis were expressed as the cross-validated r2 value (r20v). Which is defined as,
r2 cv = l-(PRESS/(I, (Y - Y mean)2)
where PRESS = ~ (Y - Yprect )2 The number of components that result in the
highest / cv and lowest standard error of predictions (SEP) were taken as the optimum. To speed up the
JUVALE et al.: QSAR STUDIES OF ANTIFUNGAL GR1SEOFULVIN ANALOGUES 197
analysis and reduce the noise, a minimum filter value "0-" of 2.0 kcallmol was used. The leave one out (LOO)21 method of cross-validation is rather obsolete and it generally gives high r2 value. To further assess the robustness and statistical confidence of the derived models, bootstrapping analysis (100 runs) was performed. The statistical results obtained for both CoMFA and CoMSIA analysis are shown in Tables 2 and 3.
Predictive r2 value (r2 pred)
To validate the derived CoMFA models, biological activities of the test set molecules were predicted
Table 2-Summary of CoM FA results with additional descriptor ClogP
1 "
2 d 0.565 r ev
Components 6
SEP 0.615 2 0.967 r con
SEE 0.169
FValue 74.307
Contrib. Steric 0.574
Electrostatic 0.221
ClogP 0.204 7
0.506 r-prcd
7. 0.987 ,. BS
"Alignment by RMS fit. b Alignment by multifit. C Alignment by field fit.
Alignments
2b 3C
0.614 0.443
6 6
0.581 0.696
0.978 0.981
0.139 0.128
110.67 1 131.135
0.527 0.574
0.251 0.221
0.222 0.205
0.619 0.618
0.989 0.988
dcross-validated 1'2 value was obtained from leave-one-out method.
Table 3-Results of CoMSIA analysis
SE SEH 2 d 0.326 0.427 r cv
Components 5 3
SEP 0.743 0.646 2 0.950 0.924 r con
SEE 0.203 0.235
F Value 60.460 72.00
Contrib. Steric 0.356 0.241
Electrostatic 0.644 0.411
Hydrophobic 0.000 0.348 2 0.620 0.680 r prcd 7
0.963 0.921 roSS
S= Steric; E= Electroststic; H= Hydrophobic d Same as in Table 2.
H
0.446
3
0.635
0.928
0.229
77.146
0
0
1
0.766
0.937
using models derived from training set. Predictive r2
value was calculated using formula r2 pred=(SDPRESS)/SD, where SD is the sum of squared deviation between the biological activities of the test set molecule and the mean activity of the training set molecules and PRESS is the sum of squared deviations between the actual and the predicted activities of the test molecules .
3D-QSAR using TSAR 3.3 All computational studies were performed using
TSAR 3.38 running on a 3.0 GHz Intel Pentium IV processor running on Windows 2000. Substituent ~ to carbonyl function was defined as substituent-l and substituent a to carbonyl function was defined as substituent-2. Various physicochemical descriptors such as total dipole moment, dipole along X, Y and Zaxis, molecular refractivity and ClogP were calculated. Similarly various sterimol descriptors like Ver/oop length and bulk (B I and B2) indices were calculated for each substituent. A total of 22 descriptors belonging to various categories such as structural, electronic, thermodynamic, spatial, topological were used for the study. Multiple linear regression (MLR) was performed to obtain correlation between these descriptors and the biological activity. The validity of the regression was confirmed by correlation coefficient, standard deviation, F-test values. The best model was selected on the basis of statistical significance.
Results Three methods were used to derive QSAR models
for griseofulvin analogues with antifungal activity. Antifungal activity was expressed as logarithm of relative antifungal activity to griseofulvin (Table 1). The alignment of the molecules was carried out using three techniques, namely, rms fitting (atom-based), multifit (flexible fitting) and SYBYL QSAR rigid body field fit. The most active molecule 21 was used as the template for alignment by considering the heavy atoms of ring as shown in Fig. 1. The superimposition of all the molecules is shown in Fig. 2. CoMFA models were generated using twenty-two molecules (1-22, Table 1), with column filtering value (0-) of 2.0. The generated models were validated using a test set of four molecules (23-26, Table 1). All the results of cross-validation were analyzed by considering the fact that a value of cross-validated / (/cv) above 0.3 indicates the probability of getting correlation value by chance is less than 5%.
198 INDIAN J CHEM, SEC A. JANUA RY 2006
Table 4--Results of analysis with cross-validati on for 2 and 5 groups and randomized biological activities
""cv fo r 2 groupsa ,.2cv for 5 groupsb Random ized ,.2 c
CoM FA" CoMSIAc CoM FA" CoMSIAc CoMFA" CoMSIAc
Mean 0.532 0.418 0.541 0.449 -0.1 85 -0.293
SO 0.086 0. 134 0.055 0.047 0.203 2.008
High 0.764 0.589 0.70 1 0.380 0.057 0.090
Low 0.439 0.1 28 0.504 0.338 -0.066 -0.648
"Cross-validated ,.2 fo r 2 groups with optimum number of components, average 25 runs. bCross-validated ,.2 for 5 groups with optimum number of components, average 25 runs. cCross-validated,.2 with randomized biological ac tivity, average of 25. "CoM FA model gene rated by multifit. cCoMSIA analysi s by combined hydrophobic, donor and acceptor fi elds.
The classical QSAR reported by Hansch et al. S
emphasized that the activity is influenced by lipophilicity of the molecules. To further account for the lipophilicity , ClogP was included in the analysis . The results obtained from the CoMFA model after inclusion of ClogP are summarized Table 2. The results of analysis with cross validation for 2 and 5 groups and randomized biological activities are given in Table 4 . The predicted antifungal activities from the best QSAR models are given in Table 5.
Multifit alignment showed good statistical results as compared to other alignments. In multifit, i:1clusion of ClogP improved the /ev from 0.226 to 0.634 and r2 pred from 0.332 to 0.614. The contour maps obtained from the multifit model with ClogP as additional descriptor are shown in Figs 3 and 4 and are used to explain the SAR of molecules in the present study. The observed versus predict biological ac tivities of the training and test sets are plotted in Fig. 5.
CoMSIA was performed using steric, electrostatic, hydrogen bond donor (HB donor) , hydrogen bond acceptor (HB acceptor) and hydrophobic fields. The alignments used for the CoMFA study served as alignments for CoMSIA. Again multifit alignment gave best CoMSIA model. The contour map obtained from CoMSIA model is shown in Fig. 6. Though all CoMSIA models showed considerable correlati ve and predictive abilities, the model having on ly hydro .. phobic field was chosen as the best model. The models showed higher r2ev (0.446) and a good r2 pretl
(0.766) values. The model, characteri zed by a lower SEE (0 .229) and a SEP (0.635), was selected as the best model to generate contour maps and explain the SAR. The results of CoMSIA are summarized in Table 3 and the observed versus predicted biological acti vi ties of the training and test sets are plotted in Fig. 7.
Table 5--Predicted antifungal activities of molecules used in the present study from best CoMFA and CoMSIA models
s'No. CoMFA CoMSIA
I 0.055 0.l63
2 0.146 -0.231
3 -0.044 -0.181
4 0.344 0.201
5 0.398 0.356
6 0.451 0.661
7 1.072 1.093
8 0.238 0.273
9 0.810 0.686
10 0.615 0.480
11 0.529 0.916
12 0.88 1.052
13 0.672 0.800
14 0.964 1.066
15 1.389 1.608
16 1.382 1.076
17 1.995 1.932
18 0.996 1.28 1
19 0.321 0.476
20 0.413 0.346
2 1 2.639 2.29 1
22 0.869 2.765
23 -0.077 -0.126
24 0. 134 0.366
25 0.769 0.789
26 0.659 0.695
Further QSAR analysis was carried out using structural, electronic, thermodynamic, spatial, topolog ical descriptors. Best QSAR contains electronic, thermodynamic and topological parameters.
Compounds 27 and 28 were over predicted in both models making them outliers. Retention of compounds 27 and 28 in the generation of model
JUYALE et al.: QSAR STUDIES OF ANTIFUNGAL GRISEOFULYIN ANALOGUES 199
Fig. 3-----CoMFA steril STDEY * contour plots from the multi fit. Sterically favored areas are represented by green polyhedra. Sterically unfavorable areas are represented by yellow polyhedra. The active molecule 21 is shown in capped-sticks.
Fig. 4-CoMFA electlOstatic STDEY*COEFF contour plots flOm multifit. Positive charge favored areas are represe nted by blue poly hedra. Negative charge favored areas are represented by red polyhedra. The act ive molecule 21 is shown in capped-sticks.
lowers the statistical significance; hence QSAR analysis was performed without it.
The best CoMF A and CoMSIA models were subjected to rigorous statistical cross validation using two and five groups. Similarly, randomization PLS analysis was carried out for the best QSAR models. The results are summarized in Table 4 . The /cv for both CoMFA and CoMSIA is greater than 0.3, which indicates that models are statistically robust. A negative / cv value from randomization PLS indicates that there is no chance correlation.
4
£3 >
1:5 ro 2 "0 Q)
~ "0 Q)
~ 0 ~~~L--r-------'--------r-------'
-1 3 4
Actual activity
Fig. 5--Graph of observed activity versus predicted activities of training se t and test set molecules flOm multi fit alignment of CoM FA analysis , activity expressed as log (MR).
Fig. 6--CoMSIA hydrophobic fi elds. Yellow indicates regions where hydrophobic substituents enhance activity; white indi cates regions where hydrophobic substituent reduces ac ti vity. The active molecule 21 is shown in capped-stick.
2
Actual activity
3 4
Fig. ' - Plot of observed activity versus predicted activities of training set and test set molecules from CoMSIA analysis with hydrophobic activity expressed as log (MR).
Discussion Classical QSAR for griseofulvin was reported by
Hansch & LienS. Due to the unavailability of suitable steric parameters for such molecules the study was limited to electronic and lipophilic parameters .
200 INDIAN J CHEM, SEC A, JANUARY 2006
Log MR=0.555(±0.17) log P+2. I 93 (±0.77) (J
-1.322(±0.61 ) n = 22, r = 0.875, Std. dev. = 0.248
It was reported that activity of griseofulvin analogues depend linearly on electronic effect and Jipophilicity of substituents a to the carbonyl function.
In order to validate the results and to find out contribution of steric factor, CoMFA and CoMSlA study was carried out. In the present study 3D-QSAR models were generated using a training set of 22 molecules. The CoMF A and CoMSIA models generated have considerable internal and external predictive abilities
CoMFA The different model s generated using steric and
electrostatic fields in CoMFA method for three different alignments were not statistically significant. As the authors of the original papers suggest, lipophilicity plays an important role along with electrostatic parameters. The additional descriptor ClogP along with steric and electrostatic fields in CoMFA improved the statistical significantly. Since the multifit alignment gave best CoMFA model, it is discussed here. The CoMFA steric and electrostatic contour maps of the most active molecule 21 are shown in Figs 3 and 4 respectively. Though CoMFA contour maps cannot be used as receptor maps, still they generate many useful interpretations.
Fig. 3 depicts the CoMFA steric contour plot. The green contours represent the regions of high steric tolerance and the yellow contours represent the regions of unfavourable steric effects. The sterically favourable green contours can be seen in the figure around ~ position of the a,~-unsaturated ketone. The compounds with larger substituents show higher activity. Compounds 21 and 22 are highly active because they contain bulky groups, which are orientated within the sterically favourable region. The unfavourable yellow contours are observed at substituents a to the carbonyl function. The compounds with small substituents are highly . active. It also suggests that the orientation of the bulky groups at these positions is important for biological activity.
Fig. 4 displays the electrostatic contour plot using CoMFA. The blue contours describe the positive charge favourable region and red contours show the negative charge favourable regions. The red contours are found near the a-substituents. These negative
charge favourable contours are flanked with positive charge favourable blue region. This red contour near substituent at a-position of a,~-unsaturated ketone emphasizes the need for negatively charged atoms for electrostatic interaction with the receptor to show good antifungal activity. The electron-rich substitution at a-position enhances antifungal activity. The compounds 3, 4, 5 are less active due to the lack of electron-rich atom to interact with the receptor through hydrogen bonding.
CoMSIA As the model with only hydrophobic parameter
gave good statistical results (Table 3), this model was used to analyze CoMSIA 3D-plots. In Fig . 6, white contours found around different functional groups of the most active compound 21 indicate the need of properly oriented groups to show potent antifungal activity. Hydrophobically disfavourable white region adjacent to substituent a to ketone function indicates that for the molecule to exhibit good antifungal activity, it should possess substructures with appropriate size and shape to occupy the hydrophobically favourable region. This is further supported by the fact that the same functional group in CoMFA analysis was surrounded by sterically unfavourable region.
3D-QSAR using TSAR 3.3 MLR analysis was performed to obtain correlation
between physicochemical descriptors and the biological activity . The validity of the regression was confirmed by correlation coefficient, standard deviation, F-test values. The best models were selected on the basis of statistical significance. The best QSAR model is given by:
Log MR=0.916 Dipole+0.899 log P +0.274 VL1 - 0.344 VLz-0.736 VB2 (1)
where n=26 ; r2=0.866; s=0.368; F=16.717 and r2cv = 0.582. VL1=Verloop length parameter for substituent 1; VL2= Verloop length parameter for substituent 2; and VB2 (1)= Verloop bulk parameter 2 for substituent 1.
The equation has electronic parameter dipole, thermodynamic parameter 10gP and Verloop sterimol length and bulk parameters, which contribute significantly to the activity . 'Dipole' represents an electronic parameter and as it is positively correlated with the actIvity, it indicates the importance of electronic interaction with the receptor site. 10gP
JUVALE e/ af. : QSAR STUDIES OF ANTIFUNGAL GRISEOFULVIN ANALOGUES 201
reflects the importance of lipophilicity. It shows positive correlation with the activity indicating that higher the lipophilicity, higher is the activity. VerJoop length for substituent one, i.e., substituent B to carbonyl function is positively correlated with activity, which indicates that a long chain substituent can give better activity. But VerJoop sterimol parameter indicating bulkiness of the same substituent shows negative correlation with activity. Overall, a suitably long but less bulky substituent is preferred at B position. Verloop length for substituent two, i.e., substituent a to carbonyl function is negatively correlated with activity, which indicates that a long substituent is sterically unfavourable for activity.
Conclusions Overall, the results of QSAR, CoMF A and
CoMSIA contour maps are complimentary with the QSAR reported by Hansch and Liens. These studies emphasize the importance of lipophilicity with respect to activity. However, the present study suggests that a small negatively charged substituent a to the ketone function and a long but less bulky substituent B to ketone function enhance the activity of griseofulvin analogues, which has not been explained by classical 2D-QSAR.
Acknowledgement AICTE, New Delhi is thanked for grant of
Emeritus Fellowship to VMK.
References I Weber K & Wehland 1, J MoL BioL, 102 (1976) 817.
2 Yamoto M, Takeuchi Y & Tomozane H, Synthesis, ( 1990) 569.
3 Tomozane H, Takeuchi Y, Cshi T, Kishida S & Yamato M, Chem Pharrn BuLL, 38 (1990) 925 .
4 Mulholland T P C, Honeywood R I W, Preston H D & Rosevear D T, J Chell1 Soc, ( 1965) 4939.
5 Hansch C & Lien E 1, J Med Chern , 14 ( 1971) 653. 6 Cramer R D Ill , Bunce 1 D & Patterson DE, Quant Struct
Act Relat, 7 (1988) 18. 7. Klebe G & Abraham U, J Cornput Aided MoL Des, 13 (1999)
l. 8 TSAR 3.3, (Accelrys Inc., San Diego, California, USA),
2000. 9 Modica M, Santagati M, Russo F, Parotti L, Gioia L D,
Selvaggini C, Salmona M & Mennini T, J Med Chern, 40 (1997) 574.
10 Kovatcheva A, Buchbauer G, Golbraikh A & Wolschann P, J Chem "~f Comput Sci, 43 (2003) 259.
II Santosh S K, Lalji K, Gediya & Kulkarni V M, Bioorg Med Chem, 7 (1999) 1475.
12 Kulkarni S S & Kulkarni V M, J Med Chern, 42 (1999) 373. 13 Murthy S V & Kulkarni V M, Bioorg Med Chetn, 10 (2002)
2267. 14 Mah indra M T & Kulkarni V M, J COll1put Aided MoL Des,
16 (2002) 18I. 15 Raichurkar A V & Kulkarni V M. J Med Chern, 46 (2003)
4419. 16 Crosse R, McWilliam R & Rhodes A, J Cen MicrobioL, 34
(1964) 5l. 17 SYBYL 6.9 .1 (Tripose Associates Inc., 1699, S: Hanley
Road, St Louis, MO 63114-2913, USA) 2003. 18 Clark M, Cramer R D III & Van Opdenbosh N, J Comput
Chern, (1989) 982. 19 Wold S, Albano C, Dunn W 1, Edlund U, Esbenson K, Gelad
P, Hellberg S, Lindburg W & Sjostrom M, in Chemetrics; Int Edn (Kowalski B Reidel, Dordrecht The Netherlands) 1984, p 17.
20 Dunn W J, Wolds U, Hellberg S & Gasteiger J, Quant Struct Act Relat Chelll BioL, 3 (1984) 3l.
21 Wold S, Technoll1 etrics, 4 (1978) 397.
