Jose Manuel Garcia de la Vega - Search Results

Year	Number of Results
2008	3
2009	1
2011	2
2012	1
2013	3
2016	1
2017	1
2019	1
2021	2
2022	0

1

Toward a Computational NMR Procedure for Modeling Dipeptide Side-Chain Conformation.

San Fabián J, Ema I, Omar S, García de la Vega JM. San Fabián J, et al. Among authors: garcia de la vega jm. J Chem Inf Model. 2021 Dec 27;61(12):6012-6023. doi: 10.1021/acs.jcim.1c00773. Epub 2021 Nov 11. J Chem Inf Model. 2021. PMID: 34762416 Free PMC article.

2

Undersampling: case studies of flaviviral inhibitory activities.

Barigye SJ, García de la Vega JM, Castillo-Garit JA. Barigye SJ, et al. Among authors: garcia de la vega jm. J Comput Aided Mol Des. 2019 Nov;33(11):997-1008. doi: 10.1007/s10822-019-00255-3. Epub 2019 Nov 26. J Comput Aided Mol Des. 2019. PMID: 31773464

3

Computational approaches to amino acid side-chain conformation using combined NMR theoretical and experimental results: leucine-67 in Desulfovibrio vulgaris flavodoxin.

San Fabián J, Omar S, García de la Vega JM. San Fabián J, et al. Among authors: garcia de la vega jm. Brief Bioinform. 2021 Sep 2;22(5):bbab020. doi: 10.1093/bib/bbab020. Brief Bioinform. 2021. PMID: 33569580

4

Anabolic and androgenic activities of 19-nor-testosterone steroids: QSAR study using quantum and physicochemical molecular descriptors.

Alvarez-Ginarte YM, Montero-Cabrera LA, de la Vega JM, Noheda-Marín P, Marrero-Ponce Y, Ruíz-García JA. Alvarez-Ginarte YM, et al. Among authors: de la vega jm. J Steroid Biochem Mol Biol. 2011 Aug;126(1-2):35-45. doi: 10.1016/j.jsbmb.2011.04.003. Epub 2011 Apr 13. J Steroid Biochem Mol Biol. 2011. PMID: 21514384

5

A theoretical approach to the solvation of brassinosteroids.

Morera-Boado C, Alonso-Becerra E, González-Jonte R, Montero-Cabrera LA, García-de-la-Vega JM. Morera-Boado C, et al. Among authors: garcia de la vega jm. J Mol Graph Model. 2009 Jan;27(5):600-10. doi: 10.1016/j.jmgm.2008.09.013. Epub 2008 Oct 14. J Mol Graph Model. 2009. PMID: 19013088

6

Chemometric and chemoinformatic analyses of anabolic and androgenic activities of testosterone and dihydrotestosterone analogues.

Alvarez-Ginarte YM, Crespo-Otero R, Marrero-Ponce Y, Noheda-Marin P, Garcia de la Vega JM, Montero-Cabrera LA, Ruiz García JA, Caldera-Luzardo JA, Alvarado YJ. Alvarez-Ginarte YM, et al. Among authors: garcia de la vega jm. Bioorg Med Chem. 2008 Jun 15;16(12):6448-59. doi: 10.1016/j.bmc.2008.04.001. Epub 2008 Apr 7. Bioorg Med Chem. 2008. PMID: 18514531

7

Electron density deformations provide new insights into the spectral shift of rhodopsins.

Hernández-Rodríguez EW, Montero-Alejo AL, López R, Sánchez-García E, Montero-Cabrera LA, de la Vega JM. Hernández-Rodríguez EW, et al. Among authors: de la vega jm. J Comput Chem. 2013 Oct 30;34(28):2460-71. doi: 10.1002/jcc.23414. Epub 2013 Aug 26. J Comput Chem. 2013. PMID: 23983208

8

Dunn's index for cluster tendency assessment of pharmacological data sets.

Rivera-Borroto OM, Rabassa-Gutiérrez M, Grau-Ábalo Rdel C, Marrero-Ponce Y, García-de la Vega JM. Rivera-Borroto OM, et al. Among authors: garcia de la vega jm. Can J Physiol Pharmacol. 2012 Apr;90(4):425-33. doi: 10.1139/y2012-002. Epub 2012 Mar 23. Can J Physiol Pharmacol. 2012. PMID: 22443093

9

Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets.

Rivera-Borroto OM, García-de la Vega JM, Marrero-Ponce Y, Grau R. Rivera-Borroto OM, et al. Among authors: garcia de la vega jm. IEEE/ACM Trans Comput Biol Bioinform. 2016 Jan-Feb;13(1):158-67. doi: 10.1109/TCBB.2015.2424435. IEEE/ACM Trans Comput Biol Bioinform. 2016. PMID: 26886740

10

Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

Rivera-Borroto OM, Marrero-Ponce Y, García-de la Vega JM, Grau-Ábalo Rdel C. Rivera-Borroto OM, et al. Among authors: garcia de la vega jm. J Chem Inf Model. 2011 Dec 27;51(12):3036-49. doi: 10.1021/ci2000083. Epub 2011 Dec 9. J Chem Inf Model. 2011. PMID: 22098113

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