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Items: 1 to 50 of 64

1.

Therapeutic alternatives in painful diabetic neuropathy: a meta-analysis of randomized controlled trials.

Vilar S, Castillo JM, Munuera Martínez PV, Reina M, Pabón M.

Korean J Pain. 2018 Oct;31(4):253-260. doi: 10.3344/kjp.2018.31.4.253. Epub 2018 Oct 1. Review.

2.

MAO inhibitory activity of bromo-2-phenylbenzofurans: synthesis, in vitro study, and docking calculations.

Delogu GL, Pintus F, Mayán L, Matos MJ, Vilar S, Munín J, Fontenla JA, Hripcsak G, Borges F, Viña D.

Medchemcomm. 2017 Jul 7;8(9):1788-1796. doi: 10.1039/c7md00311k. eCollection 2017 Sep 1.

3.

Hydroxybenzoic Acid Derivatives as Dual-Target Ligands: Mitochondriotropic Antioxidants and Cholinesterase Inhibitors.

Oliveira C, Cagide F, Teixeira J, Amorim R, Sequeira L, Mesiti F, Silva T, Garrido J, Remião F, Vilar S, Uriarte E, Oliveira PJ, Borges F.

Front Chem. 2018 Apr 23;6:126. doi: 10.3389/fchem.2018.00126. eCollection 2018.

4.

In Silico Prediction of P-glycoprotein Binding: Insights from Molecular Docking Studies.

Vilar S, Sobarzo-Sanchez E, Uriarte E.

Curr Med Chem. 2017 Nov 29. doi: 10.2174/0929867325666171129121924. [Epub ahead of print]

PMID:
29189117
5.

Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier.

Vilar S, Sobarzo-Sanchez E, Santana L, Uriarte E.

Curr Med Chem. 2018;25(9):1073-1089. doi: 10.2174/0929867324666171106163742.

PMID:
29110594
6.

Synthesis and structure-activity relationship study of novel 3-heteroarylcoumarins based on pyridazine scaffold as selective MAO-B inhibitors.

Costas-Lago MC, Besada P, Rodríguez-Enríquez F, Viña D, Vilar S, Uriarte E, Borges F, Terán C.

Eur J Med Chem. 2017 Oct 20;139:1-11. doi: 10.1016/j.ejmech.2017.07.045. Epub 2017 Jul 25.

PMID:
28797881
7.

Molecular Docking and Drug Discovery in β-Adrenergic Receptors.

Vilar S, Sobarzo-Sanchez E, Santana L, Uriarte E.

Curr Med Chem. 2017;24(39):4340-4359. doi: 10.2174/0929867324666170724101448. Review.

PMID:
28738772
8.

Coumarins and adenosine receptors: New perceptions in structure-affinity relationships.

Fonseca A, Matos MJ, Vilar S, Kachler S, Klotz KN, Uriarte E, Borges F.

Chem Biol Drug Des. 2018 Jan;91(1):245-256. doi: 10.1111/cbdd.13075. Epub 2017 Sep 4.

PMID:
28734062
9.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.

Vilar S, Friedman C, Hripcsak G.

Brief Bioinform. 2018 Sep 28;19(5):863-877. doi: 10.1093/bib/bbx010.

PMID:
28334070
10.

New insights into highly potent tyrosinase inhibitors based on 3-heteroarylcoumarins: Anti-melanogenesis and antioxidant activities, and computational molecular modeling studies.

Pintus F, Matos MJ, Vilar S, Hripcsak G, Varela C, Uriarte E, Santana L, Borges F, Medda R, Di Petrillo A, Era B, Fais A.

Bioorg Med Chem. 2017 Mar 1;25(5):1687-1695. doi: 10.1016/j.bmc.2017.01.037. Epub 2017 Jan 31.

PMID:
28189394
11.

Furvina inhibits the 3-oxo-C12-HSL-based quorum sensing system of Pseudomonas aeruginosa and QS-dependent phenotypes.

Borges A, Sousa P, Gaspar A, Vilar S, Borges F, Simões M.

Biofouling. 2017 Feb;33(2):156-168. doi: 10.1080/08927014.2017.1280732. Epub 2017 Jan 31.

PMID:
28140677
12.

Computational Drug Target Screening through Protein Interaction Profiles.

Vilar S, Quezada E, Uriarte E, Costanzi S, Borges F, Viña D, Hripcsak G.

Sci Rep. 2016 Nov 15;6:36969. doi: 10.1038/srep36969.

13.

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

Vilar S, Hripcsak G.

J Cheminform. 2016 Jul 1;8:35. doi: 10.1186/s13321-016-0147-1. eCollection 2016.

14.
15.

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

Banda JM, Callahan A, Winnenburg R, Strasberg HR, Cami A, Reis BY, Vilar S, Hripcsak G, Dumontier M, Shah NH.

Drug Saf. 2016 Jan;39(1):45-57. doi: 10.1007/s40264-015-0352-2.

16.

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

Vilar S, Lorberbaum T, Hripcsak G, Tatonetti NP.

PLoS One. 2015 Jun 12;10(6):e0129974. doi: 10.1371/journal.pone.0129974. eCollection 2015.

17.

Development of novel adenosine receptor ligands based on the 3-amidocoumarin scaffold.

Matos MJ, Vilar S, Kachler S, Celeiro M, Vazquez-Rodriguez S, Santana L, Uriarte E, Hripcsak G, Borges F, Klotz KN.

Bioorg Chem. 2015 Aug;61:1-6. doi: 10.1016/j.bioorg.2015.05.008. Epub 2015 May 22.

PMID:
26042529
18.

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Ayvaz S, Horn J, Hassanzadeh O, Zhu Q, Stan J, Tatonetti NP, Vilar S, Brochhausen M, Samwald M, Rastegar-Mojarad M, Dumontier M, Boyce RD.

J Biomed Inform. 2015 Jun;55:206-17. doi: 10.1016/j.jbi.2015.04.006. Epub 2015 Apr 24.

19.

3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance.

Vilar S, Tatonetti NP, Hripcsak G.

Sci Rep. 2015 Mar 6;5:8809. doi: 10.1038/srep08809.

20.

Systems pharmacology augments drug safety surveillance.

Lorberbaum T, Nasir M, Keiser MJ, Vilar S, Hripcsak G, Tatonetti NP.

Clin Pharmacol Ther. 2015 Feb;97(2):151-8. doi: 10.1002/cpt.2. Epub 2014 Dec 20.

21.

Potent and selective MAO-B inhibitory activity: amino- versus nitro-3-arylcoumarin derivatives.

Matos MJ, Rodríguez-Enríquez F, Vilar S, Santana L, Uriarte E, Hripcsak G, Estrada M, Rodríguez-Franco MI, Viña D.

Bioorg Med Chem Lett. 2015 Feb 1;25(3):642-8. doi: 10.1016/j.bmcl.2014.12.001. Epub 2014 Dec 8.

PMID:
25532905
22.

State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity.

Vilar S, Uriarte E, Santana L, Friedman C, Tatonetti NP.

Curr Drug Metab. 2014;15(5):490-501.

PMID:
25431152
23.

Similarity-based modeling applied to signal detection in pharmacovigilance.

Vilar S, Ryan PB, Madigan D, Stang PE, Schuemie MJ, Friedman C, Tatonetti NP, Hripcsak G.

CPT Pharmacometrics Syst Pharmacol. 2014 Sep 24;3:e137. doi: 10.1038/psp.2014.35.

24.

Similarity-based modeling in large-scale prediction of drug-drug interactions.

Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti NP.

Nat Protoc. 2014 Sep;9(9):2147-63. doi: 10.1038/nprot.2014.151. Epub 2014 Aug 14.

25.

Insight into the interactions between novel coumarin derivatives and human A3 adenosine receptors.

Matos MJ, Vilar S, Kachler S, Fonseca A, Santana L, Uriarte E, Borges F, Tatonetti NP, Klotz KN.

ChemMedChem. 2014 Oct;9(10):2245-53. doi: 10.1002/cmdc.201402205. Epub 2014 Jul 18.

PMID:
25044491
26.

Monoamine oxidase (MAO) inhibitory activity: 3-phenylcoumarins versus 4-hydroxy-3-phenylcoumarins.

Delogu GL, Serra S, Quezada E, Uriarte E, Vilar S, Tatonetti NP, Viña D.

ChemMedChem. 2014 Aug;9(8):1672-6. doi: 10.1002/cmdc.201402010. Epub 2014 Apr 29.

PMID:
24782464
27.

Insight into the functional and structural properties of 3-arylcoumarin as an interesting scaffold in monoamine oxidase B inhibition.

Matos MJ, Vilar S, García-Morales V, Tatonetti NP, Uriarte E, Santana L, Viña D.

ChemMedChem. 2014 Jul;9(7):1488-500. doi: 10.1002/cmdc.201300533. Epub 2014 Apr 8.

PMID:
24715707
28.

Synthesis, pharmacological study and docking calculations of new benzo[f]coumarin derivatives as dual inhibitors of enzymatic systems involved in neurodegenerative diseases.

Matos MJ, Janeiro P, González Franco RM, Vilar S, Tatonetti NP, Santana L, Uriarte E, Borges F, Fontenla JA, Viña D.

Future Med Chem. 2014 Mar;6(4):371-83. doi: 10.4155/fmc.14.9.

PMID:
24635520
29.

Human cytidine deaminase: a biochemical characterization of its naturally occurring variants.

Micozzi D, Carpi FM, Pucciarelli S, Polzonetti V, Polidori P, Vilar S, Williams B, Costanzi S, Vincenzetti S.

Int J Biol Macromol. 2014 Feb;63:64-74. doi: 10.1016/j.ijbiomac.2013.10.029. Epub 2013 Oct 29. Erratum in: Int J Biol Macromol. 2014 Feb;63:262.

30.

High-throughput methods for combinatorial drug discovery.

Sun X, Vilar S, Tatonetti NP.

Sci Transl Med. 2013 Oct 2;5(205):205rv1. doi: 10.1126/scitranslmed.3006667. Review.

PMID:
24089409
31.

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

Li Y, Salmasian H, Vilar S, Chase H, Friedman C, Wei Y.

J Am Med Inform Assoc. 2014 Mar-Apr;21(2):308-14. doi: 10.1136/amiajnl-2013-001718. Epub 2013 Aug 1.

32.

Recent structural advances of β1 and β2 adrenoceptors yield keys for ligand recognition and drug design.

Soriano-Ursúa MA, Trujillo-Ferrara JG, Correa-Basurto J, Vilar S.

J Med Chem. 2013 Nov 14;56(21):8207-23. doi: 10.1021/jm400471z. Epub 2013 Aug 1. Review.

PMID:
23862978
33.

MAO inhibitory activity of 2-arylbenzofurans versus 3-arylcoumarins: synthesis, in vitro study, and docking calculations.

Ferino G, Cadoni E, Matos MJ, Quezada E, Uriarte E, Santana L, Vilar S, Tatonetti NP, Yáñez M, Viña D, Picciau C, Serra S, Delogu G.

ChemMedChem. 2013 Jun;8(6):956-66. doi: 10.1002/cmdc.201300048. Epub 2013 Apr 15.

PMID:
23589499
34.

Detection of drug-drug interactions by modeling interaction profile fingerprints.

Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C.

PLoS One. 2013;8(3):e58321. doi: 10.1371/journal.pone.0058321. Epub 2013 Mar 8.

35.

Novel (coumarin-3-yl)carbamates as selective MAO-B inhibitors: synthesis, in vitro and in vivo assays, theoretical evaluation of ADME properties and docking study.

Matos MJ, Vilar S, Gonzalez-Franco RM, Uriarte E, Santana L, Friedman C, Tatonetti NP, Viña D, Fontenla JA.

Eur J Med Chem. 2013 May;63:151-61. doi: 10.1016/j.ejmech.2013.02.009. Epub 2013 Feb 16.

PMID:
23474901
36.

Application of Monte Carlo-based receptor ensemble docking to virtual screening for GPCR ligands.

Vilar S, Costanzi S.

Methods Enzymol. 2013;522:263-78. doi: 10.1016/B978-0-12-407865-9.00014-5.

PMID:
23374190
37.

Predicting monoamine oxidase inhibitory activity through ligand-based models.

Vilar S, Ferino G, Quezada E, Santana L, Friedman C.

Curr Top Med Chem. 2012;12(20):2258-74. Review.

38.

Monoamine oxidase inhibitors: ten years of docking studies.

Ferino G, Vilar S, Matos MJ, Uriarte E, Cadoni E.

Curr Top Med Chem. 2012;12(20):2145-62. Review.

PMID:
23231393
39.

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

Harpaz R, Vilar S, Dumouchel W, Salmasian H, Haerian K, Shah NH, Chase HS, Friedman C.

J Am Med Inform Assoc. 2013 May 1;20(3):413-9. doi: 10.1136/amiajnl-2012-000930. Epub 2012 Oct 31.

40.

Predicting the biological activities through QSAR analysis and docking-based scoring.

Vilar S, Costanzi S.

Methods Mol Biol. 2012;914:271-84. doi: 10.1007/978-1-62703-023-6_16.

41.

Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis.

Vilar S, Harpaz R, Santana L, Uriarte E, Friedman C.

PLoS One. 2012;7(7):e41471. doi: 10.1371/journal.pone.0041471. Epub 2012 Jul 24.

42.

Drug-drug interaction through molecular structure similarity analysis.

Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C.

J Am Med Inform Assoc. 2012 Nov-Dec;19(6):1066-74. doi: 10.1136/amiajnl-2012-000935. Epub 2012 May 30.

43.

In silico screening for agonists and blockers of the β(2) adrenergic receptor: implications of inactive and activated state structures.

Costanzi S, Vilar S.

J Comput Chem. 2012 Feb 15;33(5):561-72. doi: 10.1002/jcc.22893. Epub 2011 Dec 14.

44.

Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis.

Vilar S, Harpaz R, Chase HS, Costanzi S, Rabadan R, Friedman C.

J Am Med Inform Assoc. 2011 Dec;18 Suppl 1:i73-80. doi: 10.1136/amiajnl-2011-000417. Epub 2011 Sep 21.

45.

Delineation of the molecular mechanisms of nucleoside recognition by cytidine deaminase through virtual screening.

Costanzi S, Vilar S, Micozzi D, Carpi FM, Ferino G, Vita A, Vincenzetti S.

ChemMedChem. 2011 Aug 1;6(8):1452-8. doi: 10.1002/cmdc.201100139. Epub 2011 May 19.

46.

In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor.

Vilar S, Karpiak J, Berk B, Costanzi S.

J Mol Graph Model. 2011 Apr;29(6):809-17. doi: 10.1016/j.jmgm.2011.01.005. Epub 2011 Jan 19.

47.

Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models.

Vilar S, Ferino G, Phatak SS, Berk B, Cavasotto CN, Costanzi S.

J Mol Graph Model. 2011 Feb;29(5):614-23. doi: 10.1016/j.jmgm.2010.11.005. Epub 2010 Nov 19.

48.

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

Vilar S, Chakrabarti M, Costanzi S.

J Mol Graph Model. 2010 Jun;28(8):899-903. doi: 10.1016/j.jmgm.2010.03.010. Epub 2010 Apr 3.

49.

Structural basis of the selectivity of the beta(2)-adrenergic receptor for fluorinated catecholamines.

Pooput C, Rosemond E, Karpiak J, Deflorian F, Vilar S, Costanzi S, Wess J, Kirk KL.

Bioorg Med Chem. 2009 Dec 1;17(23):7987-92. doi: 10.1016/j.bmc.2009.10.015. Epub 2009 Oct 13.

50.

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer.

Vilar S, González-Díaz H, Santana L, Uriarte E.

J Theor Biol. 2009 Dec 7;261(3):449-58. doi: 10.1016/j.jtbi.2009.07.031. Epub 2009 Aug 3.

PMID:
19654012

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