Óscar Miguel Rivera Borroto & col.
556
5. AGRADECIMIENTOS
El primer autor (O.M.R.B.) quisiera agradecer a sus colegas y amigos Noel
Ferro, de la Universidad de Hannover (Alemania); Nelaine Mora-‐Diez, de la
Universidad Thomson Rivers (Canadá) y Lourdes Casas-‐Cardoso, de la Universidad
de Cádiz (España) por proveerle gentilmente con materiales bibliográficos útiles.
También, quisiera reconocer el trabajo altamente eficiente del consejo editorial
científico de la revista Anales de la Real Academia Nacional de Farmacia. Esta
investigación fue financiada parcialmente por el Programa de Colaboración entre
la UCLV y la institución belga VLIR-‐IUS. El programa de becas entre la Universidad
Autónoma de Madrid y la UCLV también financió parte de esta investigación.
6. REFERENCIAS
1.
Drews, J. Drug discovery: A historical perspective.
Science
2000
,
287
, 1960.
2.
Kubinyi, H. Strategies and recent technologies in drug discovery.
Pharmazie
1995
,
50
, 647.
3.
Chanda, S.; & Caldwell, J. Fulfilling the promise: Drug discovery in the postgenomic era.
Drug Discov Today
2003
,
8
, 168.
4.
Ren, J.; & Stammers, D. HIV reverse transcriptase structures: Designing new inhibitors and
understanding mechanisms of drug resistance.
Trends Pharmacol Sci
2005
,
26
, 4.
5.
Manly, C.; Louise-‐May, S.; & Hammer, J. The impact of informatics and computational
chemistry on synthesis and screening.
Drug Discov Today
2001
,
6
, 1101.
6.
Jorgensen, W. The many roles of computation in drug discovery.
Science
2004
,
303
, 1813.
7.
Xu, J.; & Hagler, A. Chemoinformatics and drug discovery.
Molecules
2002
,
7
, 566.
8.
Boobis, A.; Gundert-‐Remy, U.; Kremers, P.; Macheras, P.; & Pelkonen, O. In silico prediction
of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15.
Eur J
Pharm Sci
2002
,
17
, 183.
9.
Ekins, S.; Boulanger, B.; Swaan, P.; & Hupcey, M. Towards a new age of virtual ADME/TOX
and multidimensional drug discovery.
J Comput Aided Mol Des
2002
,
16
, 381.
10.
Bleicher, K.; Bohm, H.; Muller, K.; & Alanine, A. Hit and lead generation: Beyond high-‐
throughput screening.
Nat Rev Drug Discov
2003
,
2
, 369.
11.
DiMasi, J.; Hansen, R.; & Grabowski, H. The price of innovation: New estimates of drug
development costs.
J Health Econ
2003
,
22
, 151.
12.
Cruz-‐Monteagudo, M.; Borges, F.; & Cordeiro, M. N. D. S. Jointly handling potency and
toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and
chemoinformatics.
J Chem Inf Model
2011
,
51
, 3060.
13.
Tollman, P.; Guy, P.; Altshuler, J.; Flanagan, A.; & Steiner, M.
Revolution in R&D, How
Genomics and Genetics are Transforming the Biopharmaceutical Industry
; Group, B. C.;
Massachusetts, 2001.
14.
Bajorath, J. Integration of virtual and high-‐throughput screening.
Nat Rev Drug Discov
2002
,
1
, 882.
15.
Lazo, J.; & Wipf, P. Combinatorial chemistry and contemporary pharmacology.
J Pharmacol
Exp Ther
2000
,
293
, 705.
16.
Chen, W. L. Chemoinformatics: past, present, and future.
J Chem Inf Model
2006
,
46
, 2230.
17.
Gasteiger, J. Chemoinformatics: a new field with a long tradition.
Anal Bioanal Chem
2006
,
384
, 57.
18.
Warr, W. A. Some trends in chem (o) informatics.
Methods Mol Biol
2011
,
672
, 1.
19.
Reddy, A. S.; Pati, S. P.; Kumar, P. P.; Pradeep, H.; & Sastry, G. N. Virtual screening in drug
discovery-‐A computational perspective.
Curr Protein Pept Sc
2007
,
8
, 329.
20.
Seifert, M. H. J.; Wolf, K.; & Vitt, D. Virtual high-‐throughput in silico screening.
Biosilico
2003
,
1
, 143.
21.
Bajorath, J. Integration of virtual and high-‐throughput screening.
Nat Rev Drug Discovery
2002
,
1
, 882.
