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Chapter-4 Virtual and Focused Library Screening for Identification of Hits for BMP- Receptor Agonist

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Chapter-4 VIRTUAL AND FOCUSED LIBRARY SCREENING FOR IDENTIFICATION OF HITS

FOR BMP- RECEPTOR AGONIST

4.0 Introduction……………………………………………………………… 93

4.1.1 Steps in virtual database screening……………………………………. 94

4.1.2 Pre-processing of the databases………………………………………… 95

4.1.3 Selection by means of a target-specific pharmacophore……………… 95

4.1.4 Selection by means of receptor–ligand docking……………………… 96

4.1.5 Objectives and strategy……………………………………………….. 98

4.1.6 Materials and methods………………………………………………… 98

4.1.7 Results…………………………………………………………………… 100

4.1.8 Focused Library Generation and Virtual Screening…………………. 104

4.1.9 Conclusion………………………………………………………………. 108

Reference……………………………………………………………………… 109


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4.0 Introduction

The term virtual screening (VS or vHTS) is used to describe a process of computationally

analyzing large compound collections in order to prioritize compounds for synthesis or assay.1

Thus the ultimate objective of VS methods revolves around lead identification. In recent years

virtual screening (VS) has become an important tool in the armamentarium of modern drug

discovery. Since there is a pressing need to reduce the cost of drug discovery cycle which costs

approximately $800 million and takes 12-15 years for a molecule to ultimately reach market

(www.tufts.edu).The drug discovery efforts are revolve around the rational approaches to

identify molecules showing some activity against a target biological receptor, known as lead and

the progressive optimization of these leads to yield a compound with improved potency and

efficacy through modulation in physicochemical properties with an eye on the pharmacokinetics

and toxicological profiles in vivo. The newer technologies impacting this process are high

throughput screening (HTS), virtual high throughput screening using the state of the art

chemometric methods, combinatorial chemistry and the knowledge gained by X-ray

crystallography etc. Among these approaches the virtual high throughput screening based on

chemometric methods, including the structure based drug design, is the most economical both in

terms of time and money.2 The availability of inexpensive high-performance computing

platforms has transformed the VS process so that increasingly complex and more accurate

analyses can be performed on very large data sets.

Virtual screening (VS) is a rational strategy for the identification of novel biologically active

agents or lead with diverse chemical scaffolds. Walters et. al.3 define virtual screening as

"automatically evaluating very large libraries of compounds" using computer programs. The

process of VS can also be defined as ranking molecules in descending order of likelihood of

relevant biological activity usually accomplished by computational tools.4 The time and costs

associated with HTS can be reduced by correctly applying virtual screening and hence much

effort has been put in identifying VS approaches that assign low ranks to most of the inactive

compounds and high ranks to majority of the active compounds.1, 5, 6

This strategy coupled with drug likeness filters has a great potential in quickly delivering active

molecules with good ADME properties.7, 8 The increasing number of successful applications of


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3D-pharmacophore and docking based chemical database searching in medicinal chemistry

clearly demonstrates the utility of this approach in the modern drug discovery paradigm.7,9-13

Virtual screening is also important in a way that one can screen already known compounds for a

larger number of targets which is not possible by any other screening method due to

prohibitively high costs involved in such screening viz. HTS. The in-house and commercially

available databases of chemical compounds are typically used for the purpose of virtual

screening. The hypothesized compounds or computer generated virtual combinatorial libraries

may also be used for this purpose.

Among the VS approaches structure-based virtual screening (SB-VS) is more preferred strategy

in case where the 3D structure of protein-ligand complex are available and the virtual screening

using docking is the method of choice. 1,14-17 There are several examples of large scale distributed

processing initiatives i.e. virtual screening projects using grid computing, e.g.

FightAIDS@Home (www.fightaidsathome.scripps.edu), ScreenSaver/LifeSaver

(www.chem.ox.ac.uk/ curecancer), D2OL (www.d2ol.com/SARS) etc. The pharmacophore or

QSAR-based approaches are used as the alternative to SBVS18-20 in cases where structural

information on the protein-ligand complex is not available

4.1.1 Steps in virtual database screening:

A virtual screening protocol may make the use of both ligand based and structure based

approaches in a sequential or parallel manner. The strategies are chosen in such a manner so as to

get maximum time efficiency without sacrificing accuracy.17 Since the docking strategies are

computationally expensive, a smart VS protocol has been envisaged. In which a database

containing large number of molecules (typically millions) is preprocessed to eliminate less

interesting compounds. The pre-processing of the database involves methods like removal of

duplicates, elimination of counter ions, filtering chemically reactive groups (e.g., electrophiles,

metal chelators, Michael acceptors), undesirable atoms (e.g., organometallic complexes), and

other such groups emerging out of dissociation/protonation equilibria of acids and bases,

protomeric equilibria (e.g., imidazole), tautomeric equilibria (or predominant tautomers),

property filters, e.g. MW, lipophilicity, solubility, PSA, drug like and lead-like filters, selection

by chemical diversity, generation of correct or alternative configurations, enantiomers,


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diastereomers and generation of reliable 3D structures.21-22 This process takes very less time

being based on simple filters derived from 2D property profiles of the molecules and hence is

computationally less expensive. In the next step filtering can be done using pharmacophoric

queries to further shorten the hit list and in the last step the remaining hits can be subjected to

docking into target’s active site to get prioritized molecules for synthesis. These steps are

detailed below in brief.

4.1.2 Pre-processing of the databases

The pre-processing of the databases is critical to the quality of virtual screening hits, where

inactive and non-drug like molecules are filtered out in the initial phases using fast

computational tools. It is often preferable to limit vHTS to drug-like compounds. The ‘drug

likeness’ is used to indicate a broad range of properties or structural features that are generally

important in various stages of drug optimization, such as stability, solubility, and lipophilicity,

which influence drug’s ADME. Lipinski’s “rule-of-five”23 [Not more than 5 hydrogen bond

donors (OH and NH groups), 10 hydrogen bond acceptors (notably N and O), molecular weight

< 500 g/mol, ClogP< 5] is a well-known rule-of thumb that encodes a simple profile for orally

bioavailable compounds. On the other hand oprea’s24 leadlike filters [MW<450, -

3.5<CLogP<4.5 (i.e. -4.0<LogD7.4<4.0), no of rings<4, no of nonterminal single bonds<10, no

of hydrogen bond donors<5, no of hydrogen bond acceptors<10.] are stricter than Lipinski’s rule

and are proposed to be used in the design of novel combinatorial libraries that are aimed at a lead

discovery. These properties can be calculated quickly and can be easily applied for filtering a

large database. Additional filters like chemical stability or toxicity can also be applied on

specific chemical substructures. Efforts along this line have been undertaken by Mozziconacci et

al.25, Baurin et al.26 and Irwin et al.27 with the last one using the most exhaustive set of

acceptable drug-likeness criteria. However, as a matter of fact every rule has known exceptions

and these rules are only vague indicators of a molecule’s ultimate metabolic fate.1

4.1.3 Selection by means of a target-specific pharmacophore

A pharmacophore is a simplified 3D description of the key structural features of a set of known

ligands or of the target receptor. These features are derived from a set of ligands and, hence,

represent those features, common to the ligands that are deemed to be relevant to activity. The


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value of the pharmacophore search is that a reasonably focused query on 3-D structural grounds

can be applied relatively faster to a large database as compared to SBVS. The limitations are that

the query may rapidly become over-defined. It is often the case that a typical three or four-point

pharmacophore will be too restrictive and yield few interesting hits, whereas a slightly more

open-ended query may yield too many hits. 1, 17

4.1.4 Selection by means of receptor–ligand docking

The pharmacophore model developed in the previous chapter was used as a query for virtual

screening. The part of the virtual screening experiment was designed and exercised for the EGFR

inhibitors resulted in the discovery of NCEs for the predecided target (EGFR) was also used for

the validation of PBVS protocol as shown in the figure 4.1.28 The next stage up from a

pharmacophore search in terms of computational expense is explicit docking of the compound

database to the biological target of interest. This stage involves docking of each compound

(either as a rigid or conformationaly flexible model) into a model of the receptor that is treated

either as rigid or with limited side-chain flexibility. Generally the extents of the expected binding

site are defined to limit the search.


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Figure 4.1. An example of virtual screening protocol.28

This virtual screening strategy requires a 3D database of ligands, a 3D structure of the target

receptor (either derived experimentally or from a model built by homology), and a docking code

comprising an efficient searching algorithm and accurate scoring function. The potential

problems associated with molecular docking as a virtual screening strategy are the quality of the

docking results and the technical feasibility of processing large databases. It should be

remembered that the goal of docking is not to pick out conclusively the handful of expected hits,

but rather to pick a subset of compounds (perhaps 1 to 10 percent of the database) that will

contain significantly more hits than a randomly chosen set.


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4.1.5 Objectives and strategy

Since the number of known scaffolds showing BMP-receptor agonist activity is limited.

Therefore it was thought worthwhile to search for novel compounds which can bind to BMP

receptor or indirectly the promoters of BMP-2 which finally interact with BMP receptor to

initiate the final target gene expression resulting in antiosteoporotic effect. In the virtual

screening approach both the ligand based and receptor based approaches are to be used to

identify hits potential BMP- receptor agonist.

4.1.6 Materials and methods

After the development of the pharmacophore model the best hypothesis was selected for the

design of NCE which can be used as BMPR-agonist. The database of molecules from NCI 29

(NIH 2,50,250), GSK30 (26,784), Maybridge31 (1,25,465), Zinc 11 (~500000)32, Assinex34,

liginfo database 35 (11,92,232), Pubchem36 and self-generated focused library (300 molecules)

database was used for virtual screening to prioritize the new chemical entities (NCEs) for

synthesis and biological evaluation.

The virtual screening strategy used in the present study was executed using Molegro Virtual

Docker37, GOLD38 and Discovery studio 2.0 softwares. The Schrodinger V.0.839, Discovery

studio 2.040 and Molegro Virtual Docker were used to study the crystal structure and for the

validation of binding site. Discovery studio has extensive capabilities for data retrieval, storage,

filtering of non-drug like compounds, pharmacophore generation and database searching. The

strategy was to apply appropriate filters at the different stages of protocol to get a shorter hit list

in a time efficient manner. In the first step simple 2D property based filtering was carried out to

remove non drug like compounds. The remaining compounds were exported to Catalyst software

and 100 conformers/molecule were generated using the BEST algorithm. Thus generated

database of conformers was screened with the earlier generated pharmacophore. The top scoring

300 hits were selected and in the last step these hits (300) were docked into the BMPR-IA

binding cavity Molegro Virtual Docker37and GOLD38 softwares. The finally selected 10

compounds are proposed as BMP-2 promoters/stimulators. The steps used are detailed below in

sequential manner.


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1. The molecular database (sdf) were downloaded from the respective sources as reported

above. The database preparation was done by filtering out compounds violating the

opera’s lead likeness filters in addition to other filters as shown below.

Molecular weight limit = 450

Log P lower limit = -4

Log P upper limit = 4

Donors limit = 5

Acceptors limit = 10

Rotatable bond limit = 10

Chiral center limit = 4

Unconstrained chiral center limit = 3

Transition metals

No. of Rings limit = 4

Compounds with reactive groups.

2. The rest of the compounds were energy minimized to a gradient of 0.01 using Charm-m

force field in DS 2.5. The conformational search was done using Catalyst (Max. no. of

conformations per molecule-100, algorithm-FAST, Energy limit above global minimum-

20KCal./mol.) to generate molecular database ready for search by pharmacophoric query.

3. The database was screened for the pharmacophore generated earlier. A total of 300

different hits were selected based on their fit value.

4. These molecules (300) were docked in the BMPR-IA binding cavity by Molegro Virtual

Docker37 and GOLD38 softwares using default settings and 17 top scoring molecules

(using a rank by rank consensus scoring approach taking Gold Score, Moldock and

Rerank scores) were selected.

5. In addition the self generated library of 300 compounds was generated by using rational

drug design approach and keeping in the mind the feasibility of their synthesis were

screened for their biological activity as BMP-2 promoters. The strategy employs design

of leads based on known BMP-2 up regulator Bortezomib.


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4.1.7 Results

The PBVS protocol resulted in the prioritization of ~200 compounds out of which 10 compounds

were prioritized and procured based on the integrated PBVS followed by SBVS approach as

reported previously 28 and represented in the figure 4.1. Out of these top 10 compounds, four

compounds from natural resources were procured and analyzed for the targeted activity. The

results of which are represented in the figure 4.2. The compounds identified through rigorous

PBVS and different filters showed good osteoblast mediated ALP activity and the targeted gene

up regulation namely BMP-2, Runx2 and Col1 at pico molar level. The structures of molecules

(identified hits) are given in table 4.1, with their pharmacophore fit values and docking scores

(Gold Score, Moldock and rerank scores). These four compounds were also analyzed in-vitro

studies for validation of this model in preliminary target such as ALP activity, gene expression

Col-I, RunX2 and BMP-2 up regulation along with MTT assay for cytotoxicity. The results are

shown in the figure 4.2. The Aristolochic acid (ZINC00000052) Salicin (ZINC03847505),

Cholic acid (ZINC06858022), are active at 1pM dose also for osteoblast mediated ALP

production while Brucine (ZINC01069090) was not found that much active at 1pM but is very

significant at 100pM.


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Table 4.1 The 10 top ranked ligands from virtual screening with their fit values, Gold score,

moldock score, rerank score, and binding affinity respectively.

Comp. No.

Structure Library Code Fit Value Gold score Moldock

Score Rerank Score

Binding Affinity

1

ZINC01737804 4.81 57.02 -103.854 -74.1318 -30.4796

2

ZINC00257802 4.70 48.81 -104.954 -78.9325 -23.0148

3

ZINC27439289 4.47 51.25 -124.95 -53.6136 -28.3079

4

ZINC06528316 3.5 46.42 -93.3339 -79.1325 -21.3504

5

ZINC02386524 3.48 31.43 -101.344 -83.3254 -34.5902

6

2-((2,6-bis(hydroxy(oxido)amino)phenyl)diazenyl)-5-(diethylamino)benzenesulfonic acid

4.90 27.01 -115.551 -90.0426 -22.7215

7

ZINC01069090 3.49972 62.21 -107.607 -84.3918 -42.52955

8

ZINC00000052 4.09381 64.65 -93.5598 -72.5144 -32.20912

9

ZINC06858022 4.73371 51.25 -102.925 -49.6148 -41.9134

10 O

OH

OH

HO

O

HO

OH

ZINC03847505 3.89684 51.25 -84.3169 -67.7485 -41.79145


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Figure 4.2. Compounds stimulate osteoblast differentiation. Cultures were incubated at

different concentration (1pM, 100pM, 10nM, 1µM and 100µM) of compounds for 48 h. ALP

activity in osteoblasts was determined as described in Materials and Methods.

Furthermore, the effect of compounds was investigated on the expression of various osteogenic

genes in RCO where the compound 11 on treatment of RCO at 10nM for 72 h significantly

increased mRNA levels of Runx-2, BMP-2, and collagen (Col1) over control Figure 4.3. Out of

the three genes, Runx-2 and BMP-2 mRNA levels were significantly elevated over control at as

early as 24 h. Stimulation of mRNA levels of Runx-2 and BMP-2 genes by compounds

continued till 72 h.


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Figure 4.3. Compounds increases osteoblast function. RCO were cultured in osteoblast

differentiation medium as described above in the presence of compound (10nM) or vehicle

(control). Cells were stained with Alizarin Red-S. Photomicrographs show increased formation

of mineralized nodules by compound 11 treatment of osteoblasts compared with vehicle treated

osteoblasts (upper panel). Quantification of mineralization by extraction of Alizarin Red-S dye

(lower panel).

These results support the pharmacophore model and validate the target activity. The validated

pharmacophore model was further used for the focused library generation and prioritization of

the compounds to be synthesized was done under this model.


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4.1.8 Focused Library Generation and Virtual Screening

The focused library was designed using the fragment based method for rapid generation of

libraries incorporating substructure of the active compound (Bortezomib). The main core

considered for the design of the prototype with three H included the pyrazine nitrogen, amide

carbonyl functionality in the pyrazine-2-carboxamide and propanamido butyl boronic acid parts

of the Bortezomib. In order to mimic these features a core included 2-carbamoylbenzoic acid to

provide the three H features and N-benzyl-2-phenylethanamine furnishing substructure for two Z

features as substitute to a phenyl ring and one methyl group from the aliphatic side chain of the

Bortezomib (Figure 4.4). In order to create the library of compounds incorporating these

features, the structural modifications were made in the A, B and C parts of the designed

prototype (Figure 4.4) and the generated library of 300 compounds was analyzed through

pharmacophore mapping, top 11 leads out of 32 prioritized compounds with their fit values are

shown in the Table 4.2 in the supplementary information. Out of these 32 leads top 11 lead

molecules were grouped in Core I and were prioritized for synthesis and biological evaluation for

targeted activity. The rest of the compounds were divided into two groups viz. Core II and Core

III with average fit values 2.8 and 3.1 and also synthesized and tested for BMP-2 stimulation to

further validate the pharmacophore model in predicting these compounds in to their respective

classes viz. moderately active and/or least active figure 4.5.

Figure 4.4 Strategy employed for the generation of pharmacophore based focused library and

design of NCE’s based on common core identified.


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Structure-Based VS of the Focused Library

After the validation of pharmacophore model on test set compounds, these protocols were

applied on the 300 focused library compounds to identify top 35 ligands from 3 different cores

for synthetic priority. The cores were classified according to their fit values the top scoring core

and its analogs are summarized in the Table 4.2.

Table 4.2 The observed fit values of the designed molecules from core I on best pharmacophore

model.

Comp.no. R1 R2 Fit Value

Gold Score Moldock Score Rerank Score

11 -CH2C6H5 -OH 4.701 62.12 -157.443 -116.314

12a -CH2C6H5 -OCH2C6H5 3.83 62.54 -158.887 -67.452

12b -CH2C6H5 -OCOC6H5 3.738 65.12 -154.544 -76.543

12c -CH2C6H5 -CN 3.652 62.31 -93.404 -68.701

12d -CH2C6H5 -OCOCH3 3.444 63.21 -111.144 -74.036

12e -CH2C6H5 -OSO2C6H5CH3 3.792 59.97 -121.352 -94.161

12f H -OCOCH3 3.457 64.31 -154.298 -86.313

12g H -OSO2C6H5CH3 4.258 62.21 -132.937 -77.081

12h H -OCH2C6H5 4.724 65.09 -140.53 -73.968

13 H - 3.66 64.34 -177.743 -109.919

14 H -OH 4.629 62.31 -152.544 -104.543

The remaining two cores prioritized for synthesis based on pharmacophore mapping are shown

in the figure 4.5 and 4.6 respectively.


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Figure 4.5. The group II and III compounds with their fit values and docking scores for the

target activity.


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The pharmacophore mapping of these identified cores is shown in the figure 4.6 which explains

the rationale behind the selection of three cores for the synthetic prioritization.

Figure 4.6. Representative pharmacophore mapping of the identified Core- I, II and III.


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4.1.9 Conclusion: The present study was carried out to identify novel leads for BMP receptor

agonist using a sequential application of virtual screening methods. The total no. of ~209473129-

36 compounds from eight chemical databases was screened and the identified compounds were

presented here to guide rational drug design and to enable the discovery of novel lead structures

for BMP- receptor agonist. The four out of 10 identified leads were procured from fisher

scientific and assayed for the biological activity. These compounds showed potent activity for

ALP production and RunX-2, BMP-2 and Col1 as positive indication for antiosteoporotic

activity. Based on these results a focused library of 300 compounds was generated and screened

for top ranking leads. Out of 300 leads 35 compounds were selected as basic leads for synthetic

prioritization and further biological screening. These 35 compounds were further divided in three

groups (Core I, II and III) depending upon its ability to map on the developed pharmacophore

and affinity for the protein based on docking function. The compounds identified from this study

were analyzed by their structure based analysis for better understanding of the compounds

binding at molecular level.


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Reference:

1. Waszkowycz, B., Perkins, T.D.J., Sykes, R.A., Li, J. (2001), ‘Largescale virtual screening

for discovering leads in the postgenomic era’, IBM Syst. J., 40, pp 360-376.

2. Gasteiger, J. (2006), ‘The central role of chemoinformatics’, Chemom Intell. Lab. Sys.,

82, pp 200-209.

3. Walters, W. P., Stahl, M. T., Murcko, M. A. (1998) ‘Virtual screening – an overview’,

Drug Discovery Today, 3, pp 160–178.

4. Lowrie, J. F., Delisle, R. K., Hobbs, D. W., Diller, D. J. (2004), ‘The different strategies for

designing GPCR and kinase targeted libraries’, Combinatorial Chemistry & High

Throughput Screening, 495, pp 1386-2073.

5. Stahura, F. L., Bajorath, J. (2004), ‘Virtual screening methods that complement HTS’,

Combinatorial Chemistry & High Throughput Screening, 7, pp 259-269.

6. Jalaie, M., Shanmugasundaram, V. (2006), ‘Virtual screening: are we there yet’ Mini-

Reviews in Medicinal Chemistry, 10, pp 1159-1167.

7. Doman, T. N., McGovern, S. L., Witherbee, B. J., Kasten, T. P., Kurumbail, R., Stallings, W.

C., Connolly, D. T., Shoichet, B. K. (2002), ‘Molecular docking and high-throughput

screening for novel inhibitors of protein tyrosine phosphatase-1B’, Journal of Medicinal

Chemistry, 45, pp 2213-2221.

8. Matter H., Barighaus K-H., Naumann T., Klabunde T., Pirard B. (2001), ‘Computational

approaches towards the rational design of drug-like compound libraries’ Combinatorial

Chemistry & High Throughput Screening, 4, pp 453-475.

9. Segers, K., Sperandio, O., Sack, M., Fischer, R., Miteva, M. A., Rosing, J., Nicolaes, G. A.,

Villoutreix, B. O. (2007), ‘Design of protein membrane interaction inhibitors by virtual

ligand screening, proof of concept with the C2 domain of factor V’, Proceedings of the

National Academy of Sciences, 104, pp 12697-702..

10. Xiao, H., Feng, J. N., Yu, Z. Y., Zhang, L., Yu, M., He, X. H., Li, S., Shen, B. F., Li, Y.

(2007) ‘A5, a new small-molecule inhibitor of CD4 D1 obtained from a computer-aided

screening method, contributes to the inhibition of CD4+T-cell function’, Journal of

Biomolecular Screening, 12, pp 800–808.


110

11. Shaikh, M. S,; Mittal, A, Bharatam, P. V. (2008), ‘Design of fructose-2,6-bisphosphatase

inhibitors: A novel virtual screening approach’, Journal of Molecular Graphics and

Modelling, 26, pp 900-906.

12. Tintori, C., Manetti, F., Veljkovic, N., Perovic, V., Vercammen, J., Hayes, S., Massa, S.,

Witvrouw, M., Debyser, Z., Veljkovic, V., Botta, M. (2007) ‘Novel virtual screening

protocol based on the combined use of molecular modeling and electron-ion interaction

potential techniques to design HIV-1 integrase inhibitors’, Journal of Chemical

Information and Modeling, 47, pp 1536-1544.

13. Kher, S., Lake, K., Sircar, I., Pannala, M., Bakir, F., Zapf, J., Xu, K., Zhang, S. H., Liu, J.,

Morera, L., Sakurai, N., Jack, R., Cheng, J. F. (2007), ‘2-Aryl-N-acyl indole derivatives as

liver X receptor (LXR) agonists’, Bioorgaic and Medicinal Chemistry Letters, 17, pp

4442-4426.

14. Greer, J., Erickson, J. W., Baldwin, J. J., Varney, M. D. (1994) ‘Application of the three-

dimensional structures of protein target molecules in structure-based drug design’,

Journal of Medicinal Chemistry, 37, pp 1035-1054.

15. Bohacek, R. S., McMartin, C., Guida, W. C. (1996), ‘The art and practice of structure-

based drug design: a molecular modeling perspective’, Medicinal Research Review, 16,

pp 3-50.

16. Shoichet, B. K., Bussiere, D. E. (2000), ‘The role of macromolecular crystallography and

structure for drug discovery: advances and caveats’, Current Opinion in Drug Discovery

and Development, 3, pp 408-422.

17. Prathipati, P., Dixit, A., Saxena, A. K. (2007), ‘Integration of structure-based and ligand-

based approaches in drug design,’ Current Computer Aided Drug Design, 3, pp 341-352.

18. Neamati, N., Barchi, Jr J. (2002), ‘New paradigms in drug design and discovery’, Current

Topics in Medicinal Chemistry, 2, pp 211-227.

19. Taha, M. O., Bustanji, Y., Al-Bakri, A. G., Yousef, A.M., Taha, W.A., Bustanji, Y., Al-

Bakri, A. G., Yousef, A. M. (2007) ‘Discovery of new potent human protein tyrosine

phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico

screening’, Journal of Molecular Graphics and Modelling, 25, pp 870-884.


111

20. Maldonado, A. G., Doucet, J. P., Petitjean, M., Fan, B. T. (2006), ‘Molecular similarity and

diversity in chemoinformatics: from theory to applications’, Molecular Diversity, 10, 39-

79.

21. Kubinyi, H., (2003), ‘Modern biomedical research: an internally self-consistent universe

with little contact with medical reality?’, Nature Reviews Drug Discovery, 2, pp 151-154.

22. Saxena, A. K., Prathipati, P. (2006) ‘Collection and preparation of molecular databases

for virtual screening’, SAR and QSAR in Environmental Research, 17, pp 371-392.

23. Lipinski, C. A., Lombardo, F., Dominy, B. W., Feeney, P. J. (1997) ‘Experimental and

computational approaches to estimate solubility and permeability in drug discovery

and development settings’, Advanced Drug Delivery Reviews, 23, pp 3-25.

24. Oprea, T. I., Davis, A. W., Teague, S. J., Leeson, P. D. (2001) ‘Is there a difference

between leads and drugs? A historical perspective’, Journal of Chemical Information

and Modeling, 41, pp 1308-1315.

25. Mozziconacci, J. C. A. E.; Baurin, N.; Marot, C.; Morin-Allory, L. (2003), 9th Electronic

Conference of Computational Chemistry ECCC9, April.

26. Baurin, N., Baker, R., Richardson, C., Chen, I., Foloppe, N., Potter, A., Jordan, A.,

Roughley, S., Parratt, M., Greaney, P., Morley, D., Hubbard, R. E. (2004), ‘Drug-like

annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7

million compounds’, Journal of Chemical Information and Modeling, 44, pp 643-651.

27. Irwin, J. J., Shoichet, B. K. (2005), ‘ZINC--a free database of commercially available

compounds for virtual screening’, Journal of Chemical Information and Modeling, 45:

pp 177-182.

28. Gupta, A.K., Bhunia, S.S., Balaramnavar, V.M., Saxena, A.K. (2011), ‘Pharmacophore

modelling, molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase

inhibitors’, SAR QSAR Environmental Research. 3, pp 239-63.

29. http://cactus.nci.nih.gov/download/nci/

30. https://www.ebi.ac.uk/chemblntd/download/#tcams_dataset

31. http://www.maybridge.com/default.aspx

32. http://www. http://ligand.info/


112

33. John, J. I., Brian K. S., (2005), ‘ZINC - A Free Database of Commercially Available

Compounds for Virtual Screening’, Journal of Chemical Information and Modeling, 45,

pp 177-182.

34. http://www.asinex.com/download-zone.html.

35. Grotthuss, M., Koczyk, G., Pas, J., Wyrwicz, L.S., Rychlewski, L. (2004), ‘Ligand.Info

Small-Molecule Meta-Database" Combinatorial Chemistry & High Throughput

Screening, 8, pp 757-61.

36. http://pubchem.ncbi.nlm.nih.gov/pc_fetch/pc_fetch-help.html

37. Thomsen, R., Christensen, M.H. (2006), ‘MolDock: a new technique for high-accuracy

molecular docking. Journal of Medicinal Chemistry, 49, pp 3315–3321.

38. Jones, G., Willett, P., Glen, R. C., Leach, A. R., Taylor, R. (1997), ‘Development and

validation of a genetic algorithm for flexible docking’, Journal of Molecular Biology,

267, pp 727-748.

39. Schrödinger Software, version 8.0; Schrödinger, LLC: New York, 2005.

40. Discovery Studio, Version 1.7; Accelrys Inc. San Diego, CA, 2007.

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