Page 43 - 79_04
P. 43
Óscar
Miguel
Rivera
Borroto
&
col.
98. Ágoston,
V.;
Kaján,
L.;
Carugo,
O.;
Hegedüs,
Z.;
Vlahovicek,
K.;
&
Pongor,
S.
In
Essays
in
Bioinformatics;
Moss,
D.
S.,
Jelaska,
S.,
Pongor,
S.,
Eds.;
IOS
Press;
The
Netherland,
2005;
p
11.
99. Ellis,
D.;
Furner--Hines,
J.;
&
Willett,
P.
Measuring
the
degree
of
similarity
between
objects
in
text
retrieval
systems.
Perspect
Inf
Manag
1994,
3,
128.
100.Cuadras,
C.
M.
Distancias
estadísticas.
Estadística
Española
1989,
30,
295.
101.Willett,
P.;
Barnard,
J.
M.;
&
Downs,
G.
M.
Chemical
similarity
searching.
J
Chem
Inf
Comput
Sci
1998,
38,
983.
102.Willett,
P.
Similarity--based
virtual
screening
using
2D
fingerprints.
Drug
Discov
Today
2006,
11,
1046.
103.David,
H.
W.;
&
William,
G.
M.
No
Free
Lunch
Theorems
for
Search,
1995.
104. Wolpert,
D.
H.;
&
Macready,
W.
G.
No
free
lunch
theorems
for
optimization.
IEEE
T
Evolut
Comput
2002,
1,
67.
105.Grünwald,
P.
In
Advances
In
Minimum
Description
Length:
Theory
And
Applications;
Grünwald,
P.
D.,
Myung,
I.
J.,
Pitt,
M.
A.,
Eds.;
MIT
Press;
Cambridge,
Massachusetts,
2005;
p
3.
106.Willett,
P.
Some
heuristics
for
nearest--neighbor
searching
in
chemical
structure
files.
J
Chem
Inf
Comput
Sci
1983,
23,
22.
107.Friedman,
J.
H.;
Bentlev,
J.
L.;
&
Finkel,
R.
A.
An
algorithm
for
finding
best
matches
in--
logarithmic
expected
time.
ACM
Trans
Marh
Softw
1977,
3,
209.
108.Bentley,
J.
L.;
Weide,
B.
W.;
&
Yao,
A.
C.
Optimal
expected
time
algorithms
for
closest
point
problems.
ACM
Trans
Marh
Softw
1980,
6,
563.
109.Smeaton,
A.
F.;
&
Van
Rijsbergen,
C.
J.
The
nearest
neighbour
in
information
retrieval.
an
algorithm
using
upperbounds.
ACM
SIGIR
Forum
1981,
16,
83.
110.Murtagh,
F.
A
very
fast,
exact
nearest
neighbour
algorithm
for
use
in
information
retrieval.
Inf
Technol:
Res
Deu
1982,
1,
275.
111.Van
Marlen,
G.;
&
Van
Den
Hende,
J.
H.
Search
strategy
and
data
compression
for
a
retrieval
system
with
binary--coded
mass
spectra.
Anal
Chim
Acra
1979,
112,
143.
112.Rasmussen,
G.
T.;
Isenhour,
T.
L.;
&
Marshall,
J.
C.
Mass
spectral
library
searches
using
ion
series
data
compression.
J
Chem
Inf
Comput
Sci
1979,
19,
98.
113.Baldi,
P.;
Hirschberg,
D.
S.;
&
Nasr,
R.
J.
Speeding
up
chemical
database
searches
using
a
proximity
filter
based
on
the
logical
exclusive
OR.
J
Chem
Inf
Model
2008,
48,
1367.
114.Cao,
Y.;
Jiang,
T.;
&
Girke,
T.
Accelerated
similarity
searching
and
clustering
of
large
compound
sets
by
geometric
embedding
and
locality
sensitive
hashing.
Bioinformatics
2010,
26,
953.
115.Kearsley,
S.
K.;
Sallamack,
S.;
Fluder,
E.
M.;
Andose,
J.
D.;
Mosley,
R.
T.;
&
Sheridan,
R.
P.
Chemical
similarity
using
physiochemical
property
descriptors.
J
Chem
Inf
Comput
Sci
1996,
36,
118.
116.Ginn,
C.
M.
R.;
Willett,
P.;
&
Bradshaw,
J.
Combination
of
molecular
similarity
measures
using
data
fusion.
Perspect
Drug
Discov
Des
2000,
20,
1.
117.Hert,
J.;
Willett,
P.;
Wilton,
D.
J.;
Acklin,
P.;
Azzaoui,
K.;
Jacoby,
E.;
&
Schuffenhauer,
A.
Comparison
of
fingerprint--based
methods
for
virtual
screening
using
multiple
bioactive
reference
structures.
J
Chem
Inf
Comput
Sci
2004,
44,
1177.
118.Nasr,
R.
J.;
Swamidass,
S.
J.;
&
Baldi,
P.
F.
Large
scale
study
of
multiple--molecule
queries.
J
Cheminf
2009,
1,
1.
119.Chen,
B.;
Mueller,
C.;
&
Willett,
P.
Combination
rules
for
group
fusion
in
similarity--based
virtual
screening.
Mol
Inf
2010,
29,
533
120.Geppert,
H.;
&
Bajorath,
J.
Advances
in
2D
fingerprint
similarity
searching.
Expert
Opin
Drug
Discov
2010
5,
529.
121.Nicholls,
A.
What
do
we
know
and
when
do
we
know
it?
J
Comput--Aided
Mol
Des
2008,
22,
239.
122.Witten,
I.
H.;
&
Frank,
E.
Data
Mining
--
Practical
Machine
Learning
Tools
and
Techniques;
2nd
ed.;
Morgan
Kaufmann;
San
Francisco,
CA,
2005;
161--176.
123. Truchon,
J.;
&
Bayly,
C.
I.
Evaluating
virtual
screening
methods:
Good
and
bad
metrics
for
the
“early
recognition”
problem.
J
Chem
Inf
Model
2007,
47,
488.
124.Sheridan,
R.
P.;
Singh,
S.
B.;
Fluder,
E.
M.;
&
Kearsley,
S.
K.
Protocols
for
bridging
the
peptide
to
nonpeptide
gap
in
topological
similarity
searches.
J
Chem
Inf
Model
2001,
41,
1395.
560
