Volume 12 Supplement 14
Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors
© John et al; licensee BioMed Central Ltd. 2011
Published: 14 December 2011
Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.
Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.
The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.
A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.
Hypertension is a major factor concerning various cardiovascular diseases such as congestive cardiac failure, stroke, and myocardial infarction and affects up to 30% of the adult population in most countries . Renin is an aspartyl protease and catalytically similar to other enzymes such as pepsin, cathepsin and chymosin etc . Renin cleaves the angiotensinogen to angiotensin-I which is then converted to angiotensin-II by the action of angiotensinogen converting enzyme (ACE). Angiotensin-II is a biologically active vasopressor recognized by its receptors which is one of the cascades of events that leads to the increase in blood pressure. Renin is synthesized as prorenin, a proenzyme, which is transformed into mature renin by the cleavage of 43 amino acids long prosegment from the N-terminal end. This conversion of prorenin to renin occurs in the juxtaglomerular cells of kidney followed by the release of renin into the circulation . Renin blocks the first and rate-limiting step which is the conversion of angiotensinogen to angiotensin-I. Renin is a very specific enzyme towards its only known substrate, angiotensinogen, and this remarkable specificity makes it a very attractive and ideal target to block the renin-angiotensin system (RAS) . Inhibition of renin prevents the formation of both angiotensin-I and II but this is not the case in ACE inhibitors and angiotensin receptor blockers, which increase angiotensin-I or/and II level, respectively. Only renin inhibitors will render the complete RAS quiescent by suppressing the first step of the cascade of events. Thus, inhibition of renin would favor more complete blockade of the system . Potent inhibitors of this enzyme could therefore provide a new alternative way to treat hypertension without inhibiting other biological substances. Aspartyl protease class of enzymes contains two aspartic acid residues that are necessary for the activity. Renin enzyme has a bilobal structure similar to other aspartic proteases and an active site at the interface. The two important aspartate residues Asp32 and Asp215 catalyze the proteolytic function of renin are donated from each lobes of the enzyme . The active site of renin appears as a long, deep cleft that can accommodate seven amino acid units of the substrate, angiotensinogen, and cleaves the peptide bond between Leu10 and Val11 within angiotensinogen to generate angiotensin-I . The approaches followed to develop early renin inhibitors were based on two methodologies. One is to develop similar peptides to prorenin as this segment covers the active site of renin prior to the maturation. The second is based on the N-terminal portion of the substrate, angiotensinogen, for this binds the active site of renin. But these approaches produced only weak inhibitors . The first synthetic renin inhibitor was pepstatin. First-generation renin inhibitors were peptide analogues of the prosegment of renin or substrate analogues of the amino-terminal sequence of angiotensinogen containing the renin cleavage site .Crystal structure analyses of renin-inhibitor complexes and computational molecular modeling were later used to design selective nonpeptide renin inhibitors that lacked the extended peptide-like backbone of previous inhibitor sand had improved pharmacokinetic properties . Aliskiren is the first of these new nonpeptide inhibitors to be approved by the FDA for the treatment of hypertension but its synthesis include many steps. This invites much simpler compounds to be designed as potent renin inhibitors . Aliskiren belongs to the third generation of renin inhibitors where the large (high molecular weight) first and second generation inhibitors could not be exploited as drugs despite of their potency in vitro . To date, only few compounds were successfully developed with potent renin inhibition profiles, high efficacy, and safety. Thus designing inhibitors of high potential for renin inhibition is the most effective way to block the RAS completely. This study was focused to identify novel scaffolds with the potential to turn as the new category of renin inhibitors.
A high-correlation quantitative pharmacophore model was generated, in this study, using the observed structure-activity relationship of known renin inhibitors. We have successfully applied pharmacophore modeling, database screening, molecular docking, and density functional theory (DFT) calculation methodologies in identifying lead candidates to be employed in potent renin inhibitor design and thereby new category of anti-hypertensive agents.
Selection of training set compounds
Compounds preparation and conformation generation
The two-dimensional (2D) chemical structures of all the compounds in the data set were sketched using ChemSketch, version 12 (ACD Inc., Toronto, Canada) and subsequently converted to 3D structures in Accelrys Discovery Studio 2.5 (DS). These 3D compounds were further checked for the added hydrogens and minimized using smart minimizer that performs 1000 steps of steepest descent followed by conjugate gradient algorithms with a convergence gradient of 0.001 kcal mol-1. After energy minimization, multiple acceptable conformers were generated for every training set compound within DS Diverse Conformation Generation module using the Poling algorithm. This step was necessary to produce a good set of representative conformations of different conformation space accessible to a molecule within a given energy range. A maximum of 255 conformations were generated for each compound within an energy range of 20 kcal mol-1 above the global energy minimum [20–22].
Generation of pharmacophore models
Among the two types of ligand-based pharmacophore modeling methodologies, common feature pharmacophore modeling utilizes the common chemical features present only in the most active compounds whereas the 3D QSAR pharmacophore methodology uses the chemical features of most active and inactive compounds along with their biological activity. In this study, we have employed 3D QSAR-based pharmacophore methodology to generate pharmacophore models that can be used to estimate the activity of newly designed compounds. Feature mapping protocol as available in DS was used to identify the features that are present in the training set compounds. Uncertainty value was set to 2 and the minimum inter-feature distance was set to 2Å from the default value of 2.97 Å. As identified by the feature mapping protocol, hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), hydrophobic aliphatic (HY-AL), hydrophobic aromatic (HY-AR) and ring aromatic (RA) features were used with other default values to generate ten pharmacophore models using 3D QSAR pharmacophore generation of DS. Each feature of the resulting models occupies a certain weight that is proportional to its relative contribution to biological activity. HypoGen therefore constructs pharmacophore models correlating best with biological activities and consisting of as few features as possible. The HypoGen pharmacophore model generation process is performed in three steps such as the constructive phase, the subtractive phase and the optimization phase [23, 24]. Hypotheses that are common to the most active set of compounds are identified during the constructive phase. HypoGen calculates all possible pharmacophore configurations using all combinations of pharmacophore features for each of the conformations of the two most active compounds. Additionally, the hypotheses must fit a minimum subset of features of the remaining most active compounds in order to be considered. A large database of pharmacophore configurations is generated at the end of the constructive phase. In the subtractive phase, all pharmacophore configurations that are also present in the least active set of molecules are removed. All compounds whose activity is by default 3.5 orders of magnitude less than that of the most active compound are considered to represent the least active molecules. The value 3.5 is adjustable depending on the activity range of the training set. During the optimization phase, the hypothesis score is improved. Hypotheses are scored based on errors in activity estimates from regression and complexity. The optimization involves a variation of features and/or locations to optimize activity prediction via a simulated annealing approach. When the optimization process no longer improves the score, HypoGen stops and reports the top scoring 10 unique pharmacophores. The generated pharmacophore models were evaluated for their reliability based on the cost parameters. The overall costs of a model consist of three cost components, namely, the weight cost, the error cost, and the configuration cost. The weight component is a value that increases in a Gaussian form as this function weights in a model deviate from the ideal value of two. The error cost represents the difference between estimated and measured activities of the training set. The configuration cost quantifies the entropy of the hypothesis space.
In addition, the following three cost values are calculated during the generation of pharmacophore models: the fixed cost, the total cost, and the null cost. The fixed cost is the lowest possible cost representing a hypothetical simplest model that fits all data perfectly. Fixed costs are calculated by adding the minimum achievable error and weight cost and the constant configuration cost. Another cost parameter, the null cost, represents the maximum cost of a pharmacophore with no features and estimates activity to be the average of activity data of training set molecules. The null cost value is equal to the maximum occurring error cost. For every pharmacophore generation ten total cost values and each of fixed cost and null cost values are calculated by the pharmacophore generation protocol in the unit of bits. For a meaningful pharmacophore model, the fixed cost should be lower and the null cost should be higher and the total cost value should be closer to the fixed cost and away from the null cost value [25, 26]. HypoGen further estimates the activity of each training set compound using regression parameters. The parameters were computed by regression analysis using the relationship of geometric fit value versus the negative logarithm of activity. The better the geometric fit the greater the activity prediction of the compound. Along with these cost values, other statistical values such as correlation coefficient and root mean square deviation (RMSD) were calculated. The best pharmacophore model was selected based on the large cost difference, high correlation coefficient and lower RMSD.
The main purpose to validate the generated pharmacophore models is to investigate their ability to estimate the activity of new compounds identified through database screening or designed de novo. The selected pharmacophore model was validated using three methods based on the derived cost components, ability in test set prediction, Fischer randomization test results, and leave-one-out method. A larger difference between the fixed and null costs than that between the fixed and total costs signifies the quality of a pharmacophore model. All of these cost values are reported in bits and a difference of 40-60 bits between the total and null costs suggests a 75-90% chance of representing a true correlation in the data [27, 28]. Ninety three diverse compounds were used as the test set to validate the pharmacophore model. Fischer randomization is another approach for pharmacophore model validation. The 95% confidence level was selected in this validation study and 19 random spreadsheets were constructed. This validation method checks the correlation between the chemical structures and biological activity. This method generates pharmacophore models using the same parameters as those used to develop the original pharmacophore model by randomizing the activity data of the training set compounds. Finally the cross validation of the model was performed by using the leave-one-out methodology. In this method, 18 pharmacophore models were generated with the same parameters used for generating original pharmacophore model but leaving one compound at a time from 18 training set compounds to ensure the influence of every single training set compound in the generation of selected pharmacophore model [29, 30].
Database screening and drug-like prediction
The best pharmacophore model validated using different methods was used as a 3D query in database screening to retrieve chemical compounds that fit all the pharmacophoric features. A chemical compound must fit all the features to be picked as hits. Search 3D Database protocol with Best/Flexible search option was employed in database screening. Three chemical databases of diverse chemical compounds were screened for novel chemical scaffolds to be used in potent renin inhibitor design. The identified database hits were screened using various filters based on estimated activity, Lipinski’s rule of five , and ADMET properties [32–35].
Compounds satisfying all the filters were subjected to molecular docking studies. The GOLD (Genetic Optimization for Ligand Docking) program from Cambridge Crystallographic Data Centre, UK uses a genetic algorithm to dock the small molecules into the protein active site was used in molecular docking . GOLD allows for a full range of flexibility for the ligands and partial flexibility of the protein. Protein coordinates from the crystal structure complex of renin with aliskiren (PDB ID: 2V0Z), one of the most active inhibitors, determined at a resolution of 2.20 Å were used to define the active site. The active site was defined with a 10 Å radius around the bound inhibitor. All the water molecules except two catalytically important 184 and 250 were removed from the protein and hydrogens were added. The ten top-scoring conformations of every ligand were saved at the end of the calculation. Early termination option was used to skip the genetic optimization calculation when any five conformations of a particular compound were predicted within an RMSD value of 1.5 Å. The GOLD fitness score is calculated from the contributions of hydrogen bond and van der Waals interactions between the protein and ligand, intramolecular hydrogen bonds and strains of the ligand [37, 38]. Protein-ligand interactions were analyzed using DS and Molegro Virtual Docker  programs. The novelty of the final hits was confirmed using SciFinder  and PubChem  structure search tools.
Density functional theory (DFT) calculations
The final hits along with some most and least active compounds were used as input and all DFT calculations were carried out using Gaussian version 3.0 program. The geometry optimization of a set of compounds was carried out using the Becke3 Lee-Yang-Parr correlation functional (B3LYP), at the 6-31G* level [42–45]. The orbital energies of frontier orbitals, namely, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were calculated for a set of compounds. The calculation was performed to evaluate the electronic properties of final hits to be compared with the compounds in the training set .
Results and discussion
Statistical results of the top 10 pharmacophore hypotheses generated by HypoGen algorithm.
HBA, HBA, HBD, HY-AL
HBD, HY-AL, HY-AL, HY-AR
HBA, HBA, HY-AL, HY-AR
HBA, HBD, HY-AR, HY-AR
HBA, HBA, HY-AL, HY-AR
HBD, HBD, HY-AL, HY-AL
HBA, HBD, HY-AL, HY-AR
HBA, HBD, HY-AL, HY-AR
HBD, HBD, HY-AL, HY-AR
HBA, HBA, HBD, HY-AL
Experimental and estimated IC50 values of the training set compounds based on best pharmacophore hypothesis Hypo1.
Results of Fischer’s randomization test.
The final validation was performed using leave-one-out method, this method is used to verify whether the correlation between the experimental and predicted activities is mainly depend on one particular molecule in the training set. This is done by recomputing the pharmacophore model by excluding one molecule at a time. Consequently, 18 HypoGen calculations were carried out under the same conditions, used in the generation of original pharmacophore model Hypo1, by deriving 18 new training sets, each composed of 17 molecules. The result is positive if none of the correlation coefficients of newly generated pharmacophore models is higher or too lower to that of Hypo1. From our results it was observed that none of the 18 new models generated by this method has shown any meaningful difference compared to Hypo1 (data not shown). This result supports and increases the confident level of Hypo1 that its correlation coefficient does not depend on one particular compound in the training set. Based on these validation results, Hypo1 was used as 3D query in database screening to identify the diverse chemical compounds to be utilized in potent renin inhibitor design.
The interaction between the protein and the ligand molecules were observed using DS and Molegro Virtual Docker. The novelty of the two final hit compounds was confirmed using SciFinder search and PubChem search.
Density Functional Theory calculations
In the present work, a quantitative pharmacophore model, Hypo1, was developed based on the training set compounds with a high diversity in terms of chemical structures and biological activity values. The best pharmacophore model was selected based on various parameters such as cost difference, correlation co-efficient and validation results. Hypo1 was generated with one HY-AL, one HBD and two HBA features with a high correlation coefficient value of 0.944. The validation methods included test set prediction, Fischer randomization, and leave-one-out method. The external test set containing 93 compounds was used in validating the ability of Hypo1 in predicting the activities of compounds that are not included in training set. Hypo1 has predicted this test set with a high correlation value of 0.903. The second validation based on Fischer randomization has proved that Hypo1 was not generated by a chance correlation in the training set. The leave-one-out validation proved that the correlation coefficient of Hypo1 did not depend on one particular compound in the training set. All these validation procedures have shown the strength of the selected model, Hypo1, in predicting the active compounds. After observing the validation results, Hypo1 was used in database screening to identify hits that can be used in potent renin inhibitor design. The identified hit compounds were further filtered based on the binding mode and molecular interactions at the active site of renin. The final hits reported as potential lead compounds have scored high estimated activity, favorable drug-like properties and strong molecular interactions with the catalytic residues at the active site. The DFT calculations were performed to study the electronic properties of the hit compounds and thereby to validate the quality of the pharmacophore model, Hypo1. The final hits, HTS05096 and AW00695, showed the minimum energy gaps which represent the more reactivity of the hit compounds when compare to the most active compounds. This provided the confidence on the inhibitory property of the final hit compounds. Thus, these hits can be utilized in designing future class of novel renin inhibitors.
This research was supported by Basic Science Research Program (2009-0073267), Pioneer Research Center Program (2009-0081539), and Management of Climate Change Program (2010-0029084)through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) of Republic of Korea. And this work was also supported by the Next-Generation Bio Green 21 Program (PJ008038) from Rural Development Administration (RDA)of Republic of Korea.
This article has been published as part of BMC Bioinformatics Volume 12 Supplement 14, 2011: 22nd International Conference on Genome Informatics: Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/12?issue=S14.
- Chen ZY, Peng C, Jiao R, Wong YM, Yang N, Huang Y: Anti-hypertensive nutraceuticals and functional foods. J Agric Food Chem 2009, 57: 4485–4499. 10.1021/jf900803rView ArticlePubMedGoogle Scholar
- Kiyoshi M, Yoshihiro O, Naohisa T, Ken E, Tomohiro A, Seiji M, Shoichi O, Issei T, Kei T, Kazuwa N: Molecular cloning of a novel mouse aspartic protease-like protein that is expressed abundantly in the kidney. FEBS Lett 1997, 401: 218–222. 10.1016/S0014-5793(96)01473-1View ArticleGoogle Scholar
- Nguyen G, Delarue F, Burckle C, Bouzhir L, Giller T, Sraer JD: Pivotal role of the renin/prorenin receptor in angiotensin II production and cellular responses to renin. J Clin Invest 2002, 109: 1417–1427.PubMed CentralView ArticlePubMedGoogle Scholar
- Alan HG, Rekha P, Rishi K: Current concepts: renin inhibition in the treatment of hypertension. Curr Opin Pharmacol 2008, 8: 120–126. 10.1016/j.coph.2008.01.003View ArticleGoogle Scholar
- Yasuchika Y, Keith M, Nissim CC, Robert M, Frédéric C, Christian S, Jeanette MW, Jürgen M: The P1 N-isopropyl motif bearing hydroxyethylene dipeptide isostere analogues of aliskiren are in vitro potent inhibitors of the human aspartyl protease renin. Bio org Med Chem Lett 2009, 19: 4863–4867. 10.1016/j.bmcl.2009.05.128View ArticleGoogle Scholar
- Politi A, Durdagi S, Moutevelis-Minakakis P, Kokotos G, Papadopoulos MG, Mavromoustakos T: Application of 3D QSAR CoMFA/CoMSIA and in silico docking studies on novel renin inhibitors against cardiovascular diseases. Eur J Med Chem 2009, 44: 3703–3711. 10.1016/j.ejmech.2009.03.040View ArticlePubMedGoogle Scholar
- Alan HG, Rishi K: Renin inhibition in hypertension. J Am Coll Cardiol 2008, 51: 519–528. 10.1016/j.jacc.2007.10.027View ArticleGoogle Scholar
- Fisher ND, Hollenberg NK: Renin inhibition: what are the therapeutic opportunities? J Am Soc Nephrol 2005, 16: 592–599. 10.1681/ASN.2004100874View ArticlePubMedGoogle Scholar
- Staessen JA, Li Y, Richart T: Oral renin inhibitors. Lancet 2006, 368: 1449–1456. 10.1016/S0140-6736(06)69442-7View ArticlePubMedGoogle Scholar
- Wood JM, Maibaum J, Rahuel J, Grütter MG, Cohen NC, Rasetti V, Rüger H, Göschke R, Stutz S, Fuhrer W, Schilling W, Rigollier P, Yamaguchi Y, Cumin F, Baum HP, Schnell CR, Herold P, Mah R, Jensen C, O'Brien E, Stanton A, Bedigian MP: Structure-based design of aliskiren, a novel orally effective renin inhibitor. Biochem Biophys Res Commun 2003, 308: 698–705. 10.1016/S0006-291X(03)01451-7View ArticlePubMedGoogle Scholar
- Politi A, Durdagi S, Moutevelis-Minakakis P, Kokotos G, Mavromoustakos T: Development of accurate binding affinity predictions of novel renin inhibitors through molecular docking studies. J Mol Graph Model 2010, 29: 425–435. 10.1016/j.jmgm.2010.08.003View ArticlePubMedGoogle Scholar
- Cohen NC: Structure-based drug design and the discovery of aliskiren (Tekturna®): perseverance and creativity to overcome a R&D pipeline challenge. Chem Biol Drug Des 2007, 70: 557–565. 10.1111/j.1747-0285.2007.00599.xView ArticlePubMedGoogle Scholar
- John S, Thangapandian S, Sakkiah S, Lee KW: Identification of potent virtual leads to design novel indoleamine 2, 3-dioxygenase inhibitors: Pharmacophore modeling and molecular docking studies. Eur J Med Chem 2010, 45: 4004–4012. 10.1016/j.ejmech.2010.05.057View ArticlePubMedGoogle Scholar
- Thaisrivongs S, Pals DT, Harris DW, Kati WM, Turner SR: Design and synthesis of a potent and specific renin inhibitor with a prolonged duration of action in vivo. J Med Chem 1986, 29: 2088–2093. 10.1021/jm00160a049View ArticlePubMedGoogle Scholar
- Thaisrivongs S, Pals DT, Kati WM, Turner SR, Thomasco LM, Watt W: Design and synthesis of potent and specific renin inhibitors containing difluorostatine, difluorostatone, and related analogues. J Med Chem 1986, 29: 2080–2087. 10.1021/jm00160a048View ArticlePubMedGoogle Scholar
- Vieira E, Binggeli A, Breu V, Bur D, Fischli W, Güller R, Hirth G, Märki HP, Müller M, Oefner C, Scalone M, Stadler H, Wilhelm M, Wostl W: Substituted piperidines-highly potent renin inhibitors due to induced fit adaptation of the active site. Bioorg Med Chem Lett 1999, 9: 1397–1402. 10.1016/S0960-894X(99)00195-XView ArticlePubMedGoogle Scholar
- Bezençon O, Bur D, Weller T, Richard BS, Remen L, Sifferlen T, Corminboeuf O, Grisostomi C, Boss C, Prade L, Delahaye S, Treiber A, Strickner P, Binkert C, Hess P, Steiner B, Fischli W: Design and preparation of potent, nonpeptidic, bioavailable renin inhibitors. J Med Chem 2009, 52: 3689–3702. 10.1021/jm900022fView ArticlePubMedGoogle Scholar
- Holsworth DD, Jalaie M, Belliotti T, Cai C, Collard W, Ferreira S, Powell NA, Stier M, Zhang E, McConnell P, Mochalkin I, Ryan MJ, Bryant J, Li T, Kasani A, Subedi R, Maiti SN, Edmunds JJ: Discovery of 6-ethyl-2, 4-diaminopyrimidine-based small molecule renin inhibitors. Bioorg Med Chem Lett 2007, 17: 3575–3580. 10.1016/j.bmcl.2007.04.052View ArticlePubMedGoogle Scholar
- Sawyer TK, Pals DT, Mao B, Staples DJ, DeVaux AE, Maggiora LL, Affholter JA, Kati W, Duchamp D, Hester JB, Smith CW, Saneii HH, Kinner J, Handschumacher M, Carlson W: Design, structure-activity, and molecular modeling studies of potent renin inhibitory peptides having N-terminal Nin-For-Trp (Ftr): angiotensinogen congeners modified by P1-P1' Phe-Phe, Sta, Leu psi[CH(OH)CH2]Val or leu psi[CH2NH]Val substitutions. J Med Chem 1988, 31: 18–30. 10.1021/jm00396a006View ArticlePubMedGoogle Scholar
- Daniela S, Christian L, Theodora MS, Anja P, Rolf WH, Thierry L: Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors. J Chem Inf Model 2006, 46: 1301–1311. 10.1021/ci050237kView ArticleGoogle Scholar
- Bharatham N, Bharatham K, Lee KW: Pharmacophore identification and virtual screening for methionyl-tRNAsynthetase inhibitors. J Mol Graph Model 2007, 25: 813–823. 10.1016/j.jmgm.2006.08.002View ArticlePubMedGoogle Scholar
- Neves MAC, Dinis TCP, Colombo G, Melo MLS: An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors. Eur J Med Chem 2009, 44: 4121–4127. 10.1016/j.ejmech.2009.05.003View ArticlePubMedGoogle Scholar
- Kurogi Y, Guner OF: Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 2001, 8: 1035–1055.View ArticlePubMedGoogle Scholar
- Kansal N, Silakari O, Ravikumar M: Three dimensional pharmacophoremodelling for c-Kit receptor tyrosine kinase inhibitors. Eur J Med Chem 2010, 45: 393–404. 10.1016/j.ejmech.2009.09.013View ArticlePubMedGoogle Scholar
- Thangapandian S, John S, Sakkiah S, Lee KW: Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol Graph Model 2010, 29: 382–395. 10.1016/j.jmgm.2010.07.007View ArticlePubMedGoogle Scholar
- Sanam R, Vadivelan S, Tajne S, Narasu L, Rambabu G, Jagarlapudi SA: Discovery of potential ZAP-70 kinase inhibitors: Pharmacophore design, database screening and docking studies. Eur J Med Chem 2009, 44: 4793–4800. 10.1016/j.ejmech.2009.07.018View ArticlePubMedGoogle Scholar
- Sarma R, Sinha S, Ravikumar M, Kishore Kumar M, Mahmood SK: Pharmacophore modeling of diverse classes of p38 MAP kinase inhibitors. Eur J Med Chem 2008, 43: 2870–2876. 10.1016/j.ejmech.2008.02.014View ArticlePubMedGoogle Scholar
- Thangapandian S, John S, Sakkiah S, Lee KW: Pharmacophore-based virtual screening and Bayesian model for the identification of potential human leukotriene A4 hydrolase inhibitors. Eur J Med Chem 2010, 46: 1593–1603.View ArticleGoogle Scholar
- Friederike S, Sven L, Thomas H, Karsten S, Philip LF, Hans-dieter H: Pharmacophore definition and three-dimensional quantitative structure-activity relationship study on structurally diverse prostacyclin receptor agonists. Mol Pharmacol 2002, 62: 1103–1111. 10.1124/mol.62.5.1103View ArticleGoogle Scholar
- Daniele Z, Maria GM, Erik L, Chiara F, Caterina Z, Maurizio F, Paola P, Maria SP, Sabrina P, Luciano V: Synthesis, biological evaluation, and three-dimensional in silicopharmacophore model for σ1 receptor ligands based on a series of substituted benzo[d]oxazol-2(3h)-one derivatives. J Med Chem 2009, 52: 5380–5393. 10.1021/jm900366zView ArticleGoogle Scholar
- Ertl P, Rohde B, Selzer P: Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 2003, 43: 3714–3717.View ArticleGoogle Scholar
- Thangapandian S, John S, Sakkiah S, Lee KW: Molecular Docking and Pharmacophore Filtering in the Discovery of Dual-Inhibitors for Human Leukotriene A4 Hydrolase and Leukotriene C4 Synthase. J Chem Inf Model 2011, 51: 33–44. 10.1021/ci1002813View ArticlePubMedGoogle Scholar
- Susnow RG, Dixon SL: Use of Robust Classification Techniques for the Prediction of Human Cytochrome P450 2D6 Inhibition. J Chem Inf Comput Sci 2003, 43: 1308–1315. 10.1021/ci030283pView ArticlePubMedGoogle Scholar
- Walters WP, Murcko MA: Prediction of 'drug-likeness'. Adv. Drug. Deliv. Rev 2002, 54: 255–271. 10.1016/S0169-409X(02)00003-0View ArticlePubMedGoogle Scholar
- Cheng A, Merz K: Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem 2003, 46: 3572–3580. 10.1021/jm020266bView ArticlePubMedGoogle Scholar
- Jones G, Willett P, Glen RC: Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 1995, 254: 43–53.View ArticleGoogle Scholar
- Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD: Improved protein-ligand docking using GOLD. Proteins 2003, 52: 609–623. 10.1002/prot.10465View ArticlePubMedGoogle Scholar
- Cheng F, Wang Q, Chen M, Quiocho FA, Ma J: Molecular docking study of the interactions between the thioesterase domain of human fatty acid synthase and its ligands. Proteins 2008, 70: 1228–1234.View ArticlePubMedGoogle Scholar
- Thomsen R, Christensen MH: MolDock: A new technique for high-accuracy molecular docking. J Med Chem 2006, 49: 3315–3321. 10.1021/jm051197eView ArticlePubMedGoogle Scholar
- Wagner AB: SciFinder Scholar 2006: an empirical analysis of research topic query processing. J Chem Inf Model 2006, 46: 767–774. 10.1021/ci050481bView ArticlePubMedGoogle Scholar
- Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH: An overview of the PubChemBioAssay resource. Nucl Acids Res 2010, 38: D255-D266. 10.1093/nar/gkp965PubMed CentralView ArticlePubMedGoogle Scholar
- Lee C, Yang W, Parr RG: Development of the Colle-Salvetticonelation energy formula into a functional of the electron density. Phys Rev B 1988, 37: 785–789. 10.1103/PhysRevB.37.785View ArticleGoogle Scholar
- Becke AD: A new mixing of Hartree-Fock and local density-functional theories. J Chem Phys 1993, 98: 1372–1377. 10.1063/1.464304View ArticleGoogle Scholar
- Binkley JS, Pople JA, Hehre WJ: Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements. J Am Chem Soc 1980, 102: 939–947. 10.1021/ja00523a008View ArticleGoogle Scholar
- Gordon MS, Binkley JS, Pople JA, Pietro WJ, Hehre WJ: Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements. J Am Chem Soc 1982, 104: 2797–2803. 10.1021/ja00374a017View ArticleGoogle Scholar
- Tawari NR, Degani MS: Pharmacophore mapping and electronic feature analysis for a series of nitroaromatic compounds with antitubercular activity. J Comput Chem 2010, 31: 739–751.PubMedGoogle Scholar
- Thangapandian S, John S, Sakkiaha S, Lee KW: Potential virtual lead identification in the discovery of renin inhibitors: Application of ligand and structure-based pharmacophore modeling approaches. Eur. J Med Chem 2011, 46: 2469–2476. 10.1016/j.ejmech.2011.03.035View ArticlePubMedGoogle Scholar
- Aouchiche HA, Djennane S, Boucekkine A: DFT study of conjugated biheterocyclic oligomers exhibiting a very low homo-lumo energy gap. Synthetic Met 2004, 140: 127–133. 10.1016/S0379-6779(03)00339-4View ArticleGoogle Scholar
- Ai C, Li Y, Wang Y, Li W, Dong P, Ge G, Yang L: Investigation of binding features: effects on the interaction between CYP2A6 and inhibitors. J Comput Chem 2010, 31: 1822–1831.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.