Page 39 - 79_04
P. 39
Óscar
Miguel
Rivera
Borroto
&
col.
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.
556