ORIGINAL RESEARCH

2D-QSAR study of 1,4-benzodiazepine-2-ones as potentanti-trypanosomal agents

Neelesh Maheshwari • Anju Goyal •

Sourabh Jain

Received: 8 June 2012 / Accepted: 11 April 2013

� Springer Science+Business Media New York 2013

Abstract 2D-QSAR is one of the oldest methods of

structure activity relationship analysis which provide many

breakthroughs in the medicinal field. This method can

provide a direct correlation between various physico-

chemical properties of a molecule with its structural char-

acteristics. The correlations established by this method are

based on the relative contribution of various descriptors. In

the present study, this technique is utilized to develop a

correlation equation between the anti-human African try-

panosomiasis activity and the physicochemical parameters

depicted by various descriptors utilized within the study.

This series consists of 67 compounds with widespread range

of activity between 0.78 and 3.13 lM. In addition, an

effective correlation was obtained between SaasCE-index,

Slogp, NitrogensCount, 0PathCount, Chi4pathCluster,

CarbonsCount, and the biologic activity. These contributed

descriptors illustrate that various physicochemical proper-

ties of the molecules responsible for the activities. The

model suggests that the hydrophobicity has positive impact

on the activity, while the presence of electronegative atom

especially nitrogen and flexibility of the molecules also

provide favorable effect on the activity.

Keywords Human African trypnosomiasis � WHO �QSAR � Hydrophobicity � 1,4-Benzodiazepine-2-ones

Introduction

Human African trypanosomiasis (HAT), also known as

sleeping sickness is a parasitic disease transmitted by the

bite of the ‘‘Glossina insect’’, commonly known as Tsetse

fly (WHO, 1998). Annually one million people are at risk

due to this disease. Approximately 300,000 new cases per

year in Africa, with less than 30,000 cases diagnosed and

treated, as estimated by WHO expert committee. About

48,000 people died in 2008 caused by this disease. It has

been known for many years that chronic Trypanosomiasis

infection leads to CNS invasion by the parasite. This leads

to an inflammatory reaction within the brain which pro-

vides symptom-like psychosis leading to deteriorated sit-

uation. Currently, using therapies for HAT are several

decades old and are not surprisingly have many drawbacks

viz. high toxicity, prohibitive costs, undesirable routes of

administration as well as poor efficacy (Welburn et al.,

2009). Even though, the discovery of novel molecules and

treatment strategy are important for this disease. Hence, in

the present studies a series of 1,4-benzodiazepine-2-ones

has biologic activity in the range of 0.78–3.13 lM against

HAT considered to investigate the physiochemical features

responsible for the biologic activity (Spencer et al., 2011).

The quantitative structural activity relationship analysis

(QSAR) was employed to perform the structural feature

analysis.

QSAR has proven to be a major tool in drug discovery to

explore ligand-receptor/enzyme interactions, especially

when either the structural details of the target are unknown

or protein binding data of ligand is unavailable. 2D-QSAR

does not involve complex alignment or assumptions on

conformations; therefore, they can easily be applied to

large compound sets, both in model building and in model

application to new compounds. In such methods one has

N. Maheshwari � A. Goyal

B.N. College of Pharmacy, Udaipur, Rajasthan, India

e-mail: neeshu17@gmail.com

S. Jain (&)

School of Pharmaceutical Sciences, Rajiv Gandhi Technological

University, Airport Bypass Road Gandhinagar, Bhopal,

Madhya Pradesh, India

e-mail: jn_srv@yahoo.com

123

Med Chem Res

DOI 10.1007/s00044-013-0592-6

MEDICINALCHEMISTRYRESEARCH

the choice among a wide variety of molecular descriptors

independent on 3D conformation, e.g., topological

descriptors, simple molecular properties, e.g., molecular

weight, ClogP or atomic partial charges. Hence, in this

study the 2D QSAR analysis was performed on the 1,4-

benzodiazepine-2-ones compounds (Kubinyi et al., 1993;

Gupta and Kapoor, 1995).

Material and method

The data set compounds of 1,4-benzodiazepine-2-ones

derivatives considered for the present study is given in

Table 1 (Spencer et al., 2011). The data set comprised of

67 compounds, in which only 46 compounds have well-

defined biologic activities against the target. Hence, those

46 compounds were considered for the QSAR model

development. The biologic activities expressed as minimal

inhibitory concentration (MIC) in lM concentration, were

converted to their molar units and subsequently to free

energy related negative logarithmic state, i.e., -Log

(1/MIC50) (Golbraikh and Tropsha, 2002; Cronin and

Schultz, 2003).

Initially, data set (46 compounds) was divided into two

sets as the training set containing 25 compounds and the

remaining 21 compounds have been grouped as test set. The

test and training sets were divided randomly with the con-

sideration of equal distribution of actives or in-actives in

both sets. In this study, we have used 40 % of the com-

pounds as test set, which can effectively validate the gen-

erated model. The computational studies were performed on

V-life MDS (Molecular Design Suite)TM 3.5 (Vlifesciences,

V-Life, MDS TM 3.5, 2011, www.vlifesciences.com)

software. Each compound was subjected to energy optimi-

zation by batch optimization using Merck Molecular Force

Field (MMFF), fixing Root Mean Square Gradients (RMS)

to 0.01 kcal/mol A. The optimized batch of analogs were

selected for calculation of the physiochemical descriptors

using V-life MDS suite. The obtained descriptor pool was

reduced by eliminating out the descriptors with constant and

near-constant values. Further diminution in the descriptor

pool has been done by ousting the descriptors that are

degenerated and difficult to interpret. The remaining topo-

logical and electrotopological descriptors have been taken

into account for the correlation analysis. The descriptors,

which showed high intercorrelation and less correlation

with biologic activity was removed. Simulated annealing

methodology was applied for the variable selection. QSAR

model was generated using partial least square regression

(PLSR) method using V-life molecular design suite (MDS).

PLSR method is intensively used in QSAR analysis and this

approach leads to stable, correct, and highly predictive

models (Hoskuldsson, 1988; Eriksson et al., 2001). The rule

of thumb describe that multiple regression analysis gener-

ally requires sufficiently more compounds than parameters

(three to six times the number of parameters under con-

sideration). In the present study, four compounds for a

descriptor was adopted for limiting the number of descrip-

tors in the model (Moorthy et al., 2011a, 2012). The pro-

gram computes the best model based on squared correlation

coefficient r2, crossed validated q2, F test, and pred_r2. The

calculated Ftest value is large margin of difference with the

tabulated value at 99.99 % significance. The lower standard

error of pred_r2se, q2_se, and r2_se show absolute quality of

fitness for the QSAR model. The optimal generated QSAR

model was validated to investigate its predictive ability by

cross validation (Jack-Knife method or leave one out) and

external validation methods. These validation studies pro-

vide more statistical parameters to interpretate the predic-

tive capacity of the model. The high pred_r2 and low

pred_r2se show high predictive ability of the model. The

squared correlation coefficient (or coefficient of multiple

determination) r2, is a relative measure of quality of fitness

by the regression equation (Moorthy et al., 2011b, 2011c;

Kubinyi, 1995). Similarly, it represents the part of the

variation in the observed data explained by the regression.

The correlation coefficient values closer to 1.0 represent the

better fit of the regression.

Result and discussion

QSAR study of a series of 1,4-benzodiazepine-2-ones were

performed using partial least square regression analysis.

Among the number of models generated, the following

model has been selected as significant model for further

studies.

BA ¼ �2:2280½ � þ �0:1303½ �SaasCE-index

þ 0:6054½ �S logPþ 0:4821½ �Nitrogens Countþ �0:0759½ �0PathCountþ 0:0207½ �Chi4pathCluster

þ 0:0326½ �CarbonCount

n = 25, Degree of freedom = 18, r2 = 0.8233, q2 =

0.6605, Ftest = 93.9796, r2 se = 0.3100, q2 se = 0.4297,

pred_r2 = 0.5271, pred_r2se = 0.3980.

The derived QSAR model shows good correlation

(r2 = 0.8233) between the biologic activity and physio-

chemical descriptors such as SaasCE-index, Slog P,

Nitrogens Count, 0PathCount, Chi4pathCluster, and Car-

bonCount. The low standard error of r2_se = 0.3100

demonstrates the accuracy of the model. The significant

cross validated correlation coefficient (q2 = 0.6605) and

low q2_se = 0.4297 values reflect the internal predictive

power of the instant QSAR model. However, a high q2

value does not necessarily provide a suitable representation

Med Chem Res

123

Table 1 Inhibition data of 1,4-benzodiazepin-2-one with anti-trypanosomanl activity

N

NO

R4

R1

R2

R3

Serial no. Compound no. R1 R2 R3 R4 (MIClM) (-log10MIC)

1 5a Ph H H H 400 3.398

2 6a Ph (S)-iPr H H 100 4

3 8a Ph (S)-Bn H H 6.25 5.204

4 11a (S)-iPr H H H 410 3.387

5 12a (S)-iPr (S)-Bn H H 50 4.301

6 13a c-C6H11 (S)-iPr H H 25 4.602

7 14a c-C6H11 (S)-iPr H NO2 12.5 4.903

8 15a Ph (S)-Bn H H 12.5 4.903

9 16a 2Py (S)-Bn H H 100 4

10 18a Ph (S)-iPr Me H 100 4

11 19a Ph (S)-iPr Bn H 200 3.699

12 20a Ph (S)-iPr 2-CH2BiPh H 12.5 4.903

13 21a Ph (S)-iPr 8-CH2QUIN H 25 4.602

14 24a Ph (S)-Bn Me H 50 4.301

15 25a Ph (S)-Bn Bn H 6.25 5.204

16 26a Ph (S)-Bn 4-CH2BiPh H 6.25 5.204

17 27a Ph (S)-Bn 3-CH2BiPh H 25 4.602

18 28a Ph (S)-Bn 2-CH2BiPh H 6.25 5.204

19 29a Ph (S)-Bn 8-CH2Quin H 25 4.602

20 30a Ph (S)-Bn CH2CN H 3.1 5.509

21 32a Ph (S)-Bn CH2COOH H 50 4.301

22 35a Ph (S)-CH2OBn 4-CH2BiPh H 25 4.602

23 36a Ph (S)-CH2OBn 3-CH2BiPh H 25 4.602

24 37a Ph (S)-CH2OBn 8-CH2Quin H 12.5 4.903

25 38a iPr H Me H 400 3.398

26 39b iPr (S)-Bn Me H 25 4.602

27 40b c-C6H11 (S)-Bn Bn H 6.25 5.204

28 49b Ph (S)-Bn 4-NH2C6H4CH2 H 12.5 4.903

29 50b Ph (S)-Bn 3-NH2C6H4CH2 H 6.25 5.204

30 51b Ph (S)-Bn 2-NH2C6H4CH2 H 6.25 5.204

31 52b c-C6H11 (S)-iPr 4-NH2C6H4CH2 H 6.25 5.204

32 53b c-C6H11 (S)-iPr 4-NH2C6H4CH2 NH2 6.25 5.204

33 54b c-C6H11 (S)-Bn 4-NH2C6H4CH2 H 6.25 5.204

34 55b c-C6H11 (S)-Bn 3-NH2C6H4CH2 H 6.25 5.204

35 56b 2-Py (S)-Bn 4-NH2C6H4CH2 H 25 4.602

36 57b c-C6H11 (S)-iPr H NH2 100 4

37 58b Ph (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 0.78 6.108

38 59b Ph (S)-Bn 3-CH2C6H4–HC(=NH)NH2 H 0.78 6.108

39 60b Ph (S)-Bn 2-CH2C6H4–HC(=NH)NH2 H 1.56 5.807

40 61b c-C6H11 (S)-iPr 4-CH2C6H4–HC(=NH)NH2 H 6.25 5.204

41 62b c-C6H11 (S)-iPr 4-CH2C6H4–HC(=NH)NH2 NHC(=NH)NH2 0.78 6.108

42 63b c-C6H11 (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 0.78 6.108

Med Chem Res

123

of the real predictive power of the model for HAT inhib-

itory ligands. Hence, we have validated the model with an

external test set. The external predictive power of the

model was assessed by predicting pMIC50 values of the 21

test set molecules, which were not included in the QSAR

model development. Another parameter used to test the

predictive power of test set compound is pred_r2. The

model predicted the compounds with high pred_r2 =

0.5271 and low pred_r2se = 0.3980, showed good external

predictive power of the model. The correlation between the

observed and predicted biologic activity of both test and

training set compounds are graphically depicted in Fig. 1.

The inter-correlation among the selected descriptors was

very less due to auto scaling and cross correlation limit

permitted was 0.6. The maximum residual value for test set

compounds is 0.78 which showed (Fig. 2) good correlation

between calculated and experimental activities (Table 2).

The model incorporates six physicochemical descriptors

such as SaasCE-index, Slog P, NitrogensCount, 0PathCount,

Chi4pathCluster, and CarbonCount and their corresponding

values provided in Table 3 and graphically represented in

Fig. 3.

Fig. 1 Graph of actual versus predicted activity

Fig. 2 Residual activity of various compounds depicting the difference between actual and predicted activity

Table 1 continued

Serial no. Compound no. R1 R2 R3 R4 (MIClM) (-log10MIC)

43 64b c-C6H11 (S)-Bn 3-CH2C6H4–HC(=NH)NH2 H 1.56 5.807

44 65b 2-Py (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 3.13 5.504

45 66b c-C6H11 (S)-iPr H NHC(=NH)NH2 1.56 5.807

46 67b Ph CH2CH2CH2NHC(=NH)NH2 H H 400 3.398

Only molecule used for the development of QSAR are shown in the table according to serial number provided in the articlea denotes training set compoundb denotes test set compounds

Med Chem Res

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The descriptor SaasCE-index is an electrotopological

state indices signifies the number of carbon atoms con-

nected with one single-bond along with two aromatic

bonds. Its negative contribution in the model suggests that

those carbon atoms detrimental for the activity. In the

studied compounds, those compounds possessed such

carbon atoms attached with 1,4-benzodiazepine-2-one

nucleus exhibited low HAT inhibition, for example mol-

ecule 38 and 67 with activity 400 lM. Slog P is the log of

the octanol/water partition coefficient (including implicit

hydrogens). This property is an atomic contribution model

that calculates the log P from the given structure; i.e., the

correct protonation state (Moorthy et al., 2012). Its posi-

tive value suggests that the inclusion of hydrophobic/

aromatic substituents in the nucleus has better interaction

in the target. The highly active compounds in the series

possessed aromatic or cyclic rings in their structures

(Fig. 4).

NitrogensCount signifies the number of nitrogen atoms

in a compound and its positive contribution in the model

suggests that the presence of nitrogen atoms in the mole-

cule increases the anti-HAT activity. It is evidenced by the

presence of various phenyl guanidine substitutions in the

compounds of the series has significant activity (0.78 lM)

(Fig. 4). 0PathCount signifies total number of fragments of

zero-order (atoms) in a compound. In a very simple way, it

implies the number of substitution on carbon atom other

than H (hydrogen). This will give an account of molecular

connectivity indices or Chi indices. In more typical terms,

it can provide an estimate of sigma bonds present in the

molecule and the availability of sigma electrons (Kier and

Hall, 1986). Its negative contribution to the activity sug-

gests that there should be less number of fragments of zero-

order (atoms) in a compound or in other words more

substituted molecule will give better results in terms of

activity. The most active compounds of the series have the

least number of 0PathCounts (Fig. 4).

Chi4pathCluster signifies the molecular connectivity

index of 4th order pathcluster. In general terms, it signifies

the number of isopentane equivalent or the fourth-order

fragments attached to the molecule (Kier and Hall, 1986).

In very simple terms, it can be linked to log P value.

Increasing the substitution can rise the lipophilicity of the

molecule and hence can give positive influence toward

lipophilic receptor cavity with better interactions. A

positive value suggests that increase in the molecular

connectivity index positively influences activity. As the

descriptor signifies, the same is depicted by the most

active compounds with the highest Chi4pathClusters

(Fig. 4).

CarbonsCount signifies the number of carbon atoms in a

compound. This provides us an estimate of carbon atoms in

Table 2 Compound with their actual activity, predicted activity, and

residual activity

Serial

no.

Compound

no.

Actual

activity

Predicted

activity

Residual

activity

1 5 4.6020 4.9153 -0.3133

2 6 4.9030 4.5492 0.3538

3 8 4.9030 4.9552 -0.0522

4 11 6.1080 6.2817 -0.1737

5 12 5.2040 4.7913 0.4127

6 13 4.6020 4.8706 -0.2686

7 14 5.8070 5.8283 -0.0213

8 15 5.2040 5.2065 -0.0025

9 16 4.6020 4.8514 -0.2494

10 18 5.8070 5.4338 0.3732

11 19 4.6020 4.2526 0.3494

12 20 4.9030 4.9091 -0.0061

13 21 3.3980 3.4771 -0.0791

14 24 4.3010 4.2367 0.0643

15 25 4.0000 4.2866 -0.2866

16 26 5.2040 5.1861 0.0179

17 27 5.2040 5.1701 0.0339

18 28 4.0000 4.0041 -0.0041

19 29 3.6990 4.5330 -0.834

20 30 4.6020 4.4281 0.1739

21 32 4.3010 4.2831 0.0179

22 35 5.2040 5.0331 0.1709

23 36 4.9030 4.6945 0.2085

24 37 4.9030 4.7831 0.1199

25 38 4.6020 4.6077 -0.0057

26 39 3.3870 3.5054 -0.1184

27 40 4.0000 3.9724 0.0276

28 49 5.2040 4.7973 0.4067

29 50 4.6020 5.1678 -0.5658

30 51 5.5090 4.5794 0.9296

31 52 4.3010 3.8048 0.4962

32 53 4.6020 4.8465 -0.2445

33 54 5.2040 5.2266 -0.0226

34 55 3.3980 3.4935 -0.0955

35 56 5.2040 4.8092 0.3948

36 57 5.2040 4.9352 0.2688

37 58 5.2040 5.1993 0.0047

38 59 4.0000 4.5183 -0.5183

39 60 6.1080 5.4050 0.703

40 61 6.1080 5.5163 0.5917

41 62 5.2040 5.5543 -0.3503

42 63 6.1080 5.8184 0.2896

43 64 5.5040 5.5372 -0.0332

44 65 5.8070 5.5867 0.2203

45 66 3.3980 5.1995 -1.8015

46 67 5.2040 4.2686 0.9354

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Table 3 Compound with their descriptors values

Serial no. Compound no. Slog P SaasCE-index 0PathCount CarbonsCount Nitrogens count chi4pathCluster

1 5 2.0838 1.908148 15 12 2 2.233746

2 6 3.6426 1.94796 19 16 2 2.813308

3 8 4.0327 2.051735 21 18 2 3.237234

4 11 4.4641 2.026415 24 18 3 3.626102

5 12 4.5669 2.081084 22 19 2 2.755218

6 13 3.1287 1.978584 22 18 3 2.755218

7 14 3.525 2.967271 22 19 2 3.922112

8 15 4.6953 3.082174 25 22 2 3.760799

9 16 6.6105 6.323317 34 31 2 5.102198

10 18 4.7073 3.906093 32 28 3 5.102198

11 19 4.0592 3.011295 23 20 2 3.430516

12 20 5.2295 3.126197 26 23 2 3.269203

13 21 6.8946 5.088241 35 32 2 4.486059

14 24 7.1447 6.40133 35 32 2 4.552769

15 25 7.1447 6.393146 35 32 2 4.610602

16 26 5.2415 3.960983 33 29 3 4.610602

17 27 3.9529 2.648154 25 21 3 3.269203

18 28 3.514 2.305762 26 21 2 3.487248

19 29 6.7712 6.225565 37 33 2 4.600881

20 30 6.7712 6.179926 37 33 2 4.552769

21 32 4.868 3.778195 35 30 3 4.610602

22 35 2.1081 2.030833 16 13 2 3.016991

23 36 3.6669 2.070645 20 17 2 3.488605

24 37 5.7615 2.302892 26 23 2 3.269203

25 38 5.0599 4.696872 30 26 3 4.06632

26 39 2.4761 2.818151 18 15 2 2.175657

27 40 5.0599 4.623982 30 26 3 4.002376

28 49 5.0599 4.525576 30 26 3 4.25014

29 50 5.0577 3.929108 29 25 3 4.557916

30 51 4.6399 4.410669 30 25 4 4.911049

31 52 5.5919 3.974662 30 26 3 4.06632

32 53 5.5919 3.909391 30 26 3 4.002376

33 54 4.4549 4.320482 30 25 4 4.06632

34 55 3.6149 2.592203 22 18 3 3.590367

35 56 4.7830 4.579382 33 27 5 4.300301

36 57 4.7830 4.484231 33 27 5 4.247328

37 58 3.5007 2.833845 21 18 2 3.237234

38 59 4.7830 4.371874 33 27 5 4.386805

39 60 4.7808 3.832753 32 26 5 4.791896

40 61 4.0862 4.140346 36 27 8 5.389981

41 62 5.3150 3.878306 33 27 5 4.300301

42 63 5.3150 3.793991 33 27 5 4.247328

43 64 4.1780 4.202993 33 26 6 4.300301

44 65 4.7808 3.578289 32 26 5 4.753341

45 66 4.0827 3.177635 32 26 5 3.946747

46 67 4.0349 2.877869 22 19 2 2.755218

Med Chem Res

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the molecule with a particular arrangement that are

responsible for the activity while the nature of carbon

atoms is well known. However, this descriptor can provide

a correlation between the number of carbon atoms, their

nature and activity. The positive contribution of Carbons-

Count indicates that increasing the number of carbon atoms

positively influences the activity and increasing log P. This

is obvious that the most active molecules of the series have

highest number of carbon atoms (Fig. 4).

Conclusion

Summarizing the above information, it may be inferred

that 2D-QSAR model is generated with 1,4-benzodiaze-

pine-2-ones derivatives against HAT inhibitory activity

having reliable predictive power. The validation methods

provided significant statistical parameters with q2 [ 0.5,

which shows that the models is considered as predictive

model. In addition, low error values (root mean square

error, standard deviation) support the model and increase

its significance. The result of the study suggests that

increase in log P, number of nitrogen containing substi-

tutions (Nitrogen count), number of 3� carbon substitu-

tions (molecular connectivity index of fourth order), and

number of carbon atoms (Carboncount) will increase anti-

HAT activity. The carbon atoms connected with one sin-

gle-bond along with two atomic bonds can have a nega-

tive influence on activity but relatively to a lesser extent.

But fragments of zero-order (atoms) in a compound will

negatively influence activity. The findings derived from

this analysis along with other molecular modeling studies

will be helpful in designing of the new potent HAT

inhibitors of clinical utility.

Acknowledgments All the author gratefully acknowledge V-life

sciences for providing there valuable software for such study. One

of the author Sourabh Jain wishes to thanks AICTE New Delhi

for providing post Graduate scholarship during the period. We

also acknowledge Dr. N�S. Hari Narayana Moorthy, Professor,

Fig. 3 Contribution chart of

various descriptors utilized in

the study

Fig. 4 Most active compounds of the series

Med Chem Res

123

Department of Chemistry & Biochemistry, Faculty of Sciences,

University of Porto, 687, Rua de Campo Alegre, Porto-4169-007,

Portugal for providing their support in editing and proofreading of

this article.

References

Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct

(Theochem) 622:39–51

Eriksson L, Johansson E, Kettaneh WN, Wold S (2001) Multi- and

megavariate data analysis: principles and applications. UMetrics,

Umea

Golbraikh A, Tropsha AJ (2002) Beware of Q2! Mol Graph Model

20:269–276

Gupta SC, Kapoor VK (1995) Fundamentals of mathematical

statistics. Sulthan Chand and Sons, New Delhi, pp 3.1–3.25

Hoskuldsson A (1988) PLS regression methods. J Chemom 2(3):

211–228

Kier LB, Hall LH (1986) Molecular connectivity in structure activity

analysis. John Wily & Sons Inc, New York, p 62

Kubinyi H (1995) In medicinal chemistry & drug discovery, vol I.

John Wily & Sons Inc, New York, p 497

Kubinyi H, Mannhold R, Krogsgaard LR, Timmerman HE (1993)

Methods and principles in medicinal chemistry, vol 1. VCH,

Weinheim, pp 91–95

Moorthy NSHN, Ramos MJ, Fernandes PA (2011a) Structural

analysis of a-glucosidase inhibitors by validated QSAR models

using topological and hydrophobicity based descriptors. Che-

mom Intell Lab Sys 109:101–112

Moorthy NSHN, Ramos MJ, Fernandes PA (2011b) Topological,

hydrophobicity and other descriptors on a-glucosidase inhibition:

a QSAR study on xanthone derivatives. J Enz Inhibit Med Chem

26(6):755–766

Moorthy NSHN, Sousa SF, Ramos MJ, Fernandes PA (2011c) In

silico based structural analysis of arylthiophene derivatives for

FTase inhibitory activity, hERG and other toxic effects. J Biomol

Screen 16(9):1037–1046

Moorthy NSHN, Ramos MJ, Fernandes PA (2012) Analysis of van

der Waals surface area properties for human ether-a-go-go-

related gene blocking activity: computational study on structur-

ally diverse compounds. SAR QSAR Environ Res 23:521–536

Spencer J, Rathnam RP, Harvey AL et al (2011) Synthesis and

biological evaluation of 1,4-benzodiazepin-2-ones with antitry-

panosomal activity. Bioorg Med Chem 19(5):1802–1815

Welburn SC, Maudlin I, Simarro PP (2009) Controlling sleeping

sickness—a review. Parasitology 136:1943–1949

WHO (1998) Control and surveillance of African trypanosomiasis.

World Health Organization, Geneva

Med Chem Res

123