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@comment{{Command line: bib2bib ../bibli.bib -c 'subject:"chemogenomics" or keywords:"chemogenomics"' -ob tmp.bib}}
@article{Avlani2007Critical,
author = {Avlani, V. A. and Gregory, K. J. and Morton, C. J. and Parker, M.
W. and Sexton, P. M. and Christopoulos, A.},
title = {Critical role for the second extracellular loop in the binding of
both orthosteric and allosteric G protein-coupled receptor ligands.},
journal = {J. Biol. Chem.},
year = {2007},
volume = {282},
pages = {25677--25686},
number = {35},
month = {Aug},
abstract = {The second extracellular (E2) loop of G protein-coupled receptors
(GPCRs) plays an essential but poorly understood role in the binding
of non-peptidic small molecules. We have utilized both orthosteric
ligands and allosteric modulators of the M2 muscarinic acetylcholine
receptor, a prototypical Family A GPCR, to probe possible E2 loop
binding dynamics. We developed a homology model based on the crystal
structure of bovine rhodopsin and predicted novel cysteine substitutions
that should dramatically reduce E2 loop flexibility via disulfide
bond formation and significantly inhibit the binding of both types
of ligands. This prediction was validated experimentally using radioligand
binding, dissociation kinetics, and cell-based functional assays.
The results argue for a flexible "gatekeeper" role of the E2 loop
in the binding of both allosteric and orthosteric GPCR ligands.},
doi = {10.1074/jbc.M702311200},
keywords = {chemogenomics},
owner = {laurent},
pii = {M702311200},
pmid = {17591774},
timestamp = {2008.07.21},
url = {http://dx.doi.org/10.1074/jbc.M702311200}
}
@article{Balakin2002Property-based,
author = {Balakin, K. V. and Tkachenko, S. E. and Lang, S. A. and Okun, I.
and Ivashchenko, A. A. and Savchuk, N. P.},
title = {Property-based design of {GPCR}-targeted library.},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2002},
volume = {42},
pages = {1332--1342},
number = {6},
abstract = {The design of a GPCR-targeted library, based on a scoring scheme for
the classification of molecules into "GPCR-ligand-like" and "non-GPCR-ligand-like",
is outlined. The methodology is a valuable tool that can aid in the
selection and prioritization of potential GPCR ligands for bioscreening
from large collections of compounds. It is based on the distillation
of knowledge from large databases of GPCR and non-GPCR active agents.
The method employed a set of descriptors for encoding the molecular
structures and by training of a neural network for classifying the
molecules. The molecular requirements were profiled and validated
by using available databases of GPCR- and non-GPCR-active agents
[5736 diverse GPCR-active molecules and 7506 diverse non-GPCR-active
molecules from the Ensemble Database (Prous Science, 2002)]. The
method enables efficient qualification or disqualification of a molecule
as a potential GPCR ligand and represents a useful tool for constraining
the size of GPCR-targeted libraries that will help speed up the development
of new GPCR-active drugs.},
keywords = {chemogenomics},
owner = {laurent},
pii = {ci025538y},
pmid = {12444729},
timestamp = {2007.09.22}
}
@article{Becker2004G,
author = {Becker, O. M. and Marantz, Y. and Shacham, S. and Inbal, B. and Heifetz,
A. and Kalid, O. and Bar-Haim, S. and Warshaviak, D. and Fichman,
M. and Noiman, S.},
title = {G protein-coupled receptors: in silico drug discovery in {3D}},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2004},
volume = {101},
pages = {11304--11309},
number = {31},
month = {Aug},
abstract = {The application of structure-based in silico methods to drug discovery
is still considered a major challenge, especially when the x-ray
structure of the target protein is unknown. Such is the case with
human G protein-coupled receptors (GPCRs), one of the most important
families of drug targets, where in the absence of x-ray structures,
one has to rely on in silico 3D models. We report repeated success
in using ab initio in silico GPCR models, generated by the predict
method, for blind in silico screening when applied to a set of five
different GPCR drug targets. More than 100,000 compounds were typically
screened in silico for each target, leading to a selection of <100
"virtual hit" compounds to be tested in the lab. In vitro binding
assays of the selected compounds confirm high hit rates, of 12-21\%
(full dose-response curves, Ki < 5 microM). In most cases, the best
hit was a novel compound (New Chemical Entity) in the 1- to 100-nM
range, with very promising pharmacological properties, as measured
by a variety of in vitro and in vivo assays. These assays validated
the quality of the hits as lead compounds for drug discovery. The
results demonstrate the usefulness and robustness of ab initio in
silico 3D models and of in silico screening for GPCR drug discovery.},
doi = {10.1073/pnas.0401862101},
keywords = {chemogenomics},
owner = {laurent},
pii = {0401862101},
pmid = {15277683},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1073/pnas.0401862101}
}
@article{Bissantz2003Protein-based,
author = {Bissantz, C. and Bernard, P. and Hibert, M. and Rognan, D.},
title = {Protein-based virtual screening of chemical databases. {II}. Are
homology models of {G}-Protein Coupled Receptors suitable targets?},
journal = {Proteins},
year = {2003},
volume = {50},
pages = {5--25},
number = {1},
month = {Jan},
abstract = {The aim of the current study is to investigate whether homology models
of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin
are reliable enough to be used for virtual screening of chemical
databases. Starting from the recently described 2.8 A-resolution
X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound"
form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor,
vasopressin V1a receptor) were constructed. The homology models were
used to screen three-dimensional databases using three different
docking programs (Dock, FlexX, Gold) in combination with seven scoring
functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based
homology models turned out to be suitable, indeed, for virtual screening
since known antagonists seeded in the test databases could be distinguished
from randomly chosen molecules. However, such models are not accurate
enough for retrieving known agonists. To generate receptor models
better suited for agonist screening, we developed a new knowledge-
and pharmacophore-based modeling procedure that might partly simulate
the conformational changes occurring in the active site during receptor
activation. Receptor coordinates generated by this new procedure
are now suitable for agonist screening. We thus propose two alternative
strategies for the virtual screening of GPCR ligands, relying on
a different set of receptor coordinates (antagonist-bound and agonist-bound
states).},
doi = {10.1002/prot.10237},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12471595},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1002/prot.10237}
}
@article{Bock2005Virtual,
author = {Bock, J. R. and Gough, D. A.},
title = {Virtual screen for ligands of orphan {G} protein-coupled receptors.},
journal = {J. Chem. Inform. Model.},
year = {2005},
volume = {45},
pages = {1402--1414},
number = {5},
abstract = {This paper describes a virtual screening methodology that generates
a ranked list of high-binding small molecule ligands for orphan G
protein-coupled receptors (oGPCRs), circumventing the requirement
for receptor three-dimensional structure determination. Features
representing the receptor are based only on physicochemical properties
of primary amino acid sequence, and ligand features use the two-dimensional
atomic connection topology and atomic properties. An experimental
screen comprised nearly 2 million hypothetical oGPCR-ligand complexes,
from which it was observed that the top 1.96\% predicted affinity
scores corresponded to "highly active" ligands against orphan receptors.
Results representing predicted high-scoring novel ligands for many
oGPCRs are presented here. Validation of the method was carried out
in several ways: (1) A random permutation of the structure-activity
relationship of the training data was carried out; by comparing test
statistic values of the randomized and nonshuffled data, we conclude
that the value obtained with nonshuffled data is unlikely to have
been encountered by chance. (2) Biological activities linked to the
compounds with high cross-target binding affinity were analyzed using
computed log-odds from a structure-based program. This information
was correlated with literature citations where GPCR-related pathways
or processes were linked to the bioactivity in question. (3) Anecdotal,
out-of-sample predictions for nicotinic targets and known ligands
were performed, with good accuracy in the low-to-high "active" binding
range. (4) An out-of-sample consistency check using the commercial
antipsychotic drug olanzapine produced "active" to "highly-active"
predicted affinities for all oGPCRs in our study, an observation
that is consistent with documented findings of cross-target affinity
of this compound for many different GPCRs. It is suggested that this
virtual screening approach may be used in support of the functional
characterization of oGPCRs by identifying potential cognate ligands.
Ultimately, this approach may have implications for pharmaceutical
therapies to modulate the activity of faulty or disease-related cellular
signaling pathways. In addition to application to cell surface receptors,
this approach is a generalized strategy for discovery of small molecules
that may bind intracellular enzymes and involve protein-protein interactions.},
doi = {10.1021/ci050006d},
pdf = {../local/Bock2005Virtual.pdf},
file = {Bock2005Virtual.pdf:Bock2005Virtual.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16180917},
timestamp = {2007.07.30},
url = {http://dx.doi.org/10.1021/ci050006d}
}
@article{Bockaert1999Molecular,
author = {Bockaert, J. and Pin, J. P.},
title = {Molecular tinkering of {G} protein-coupled receptors: an evolutionary
success},
journal = {EMBO J.},
year = {1999},
volume = {18},
pages = {1723--1729},
number = {7},
month = {Apr},
abstract = {Among membrane-bound receptors, the G protein-coupled receptors (GPCRs)
are certainly the most diverse. They have been very successful during
evolution, being capable of transducing messages as different as
photons, organic odorants, nucleotides, nucleosides, peptides, lipids
and proteins. Indirect studies, as well as two-dimensional crystallization
of rhodopsin, have led to a useful model of a common 'central core',
composed of seven transmembrane helical domains, and its structural
modifications during activation. There are at least six families
of GPCRs showing no sequence similarity. They use an amazing number
of different domains both to bind their ligands and to activate G
proteins. The fine-tuning of their coupling to G proteins is regulated
by splicing, RNA editing and phosphorylation. Some GPCRs have been
found to form either homo- or heterodimers with a structurally different
GPCR, but also with membrane-bound proteins having one transmembrane
domain such as nina-A, odr-4 or RAMP, the latter being involved in
their targeting, function and pharmacology. Finally, some GPCRs are
unfaithful to G proteins and interact directly, via their C-terminal
domain, with proteins containing PDZ and Enabled/VASP homology (EVH)-like
domains.},
doi = {10.1093/emboj/18.7.1723},
keywords = {chemogenomics},
owner = {laurent},
pmid = {10202136},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1093/emboj/18.7.1723}
}
@article{Bredel2004Chemogenomics,
author = {Bredel, M. and Jacoby, E.},
title = {Chemogenomics: an emerging strategy for rapid target and drug discovery.},
journal = {Nat. Rev. Genet.},
year = {2004},
volume = {5},
pages = {262--275},
number = {4},
month = {Apr},
doi = {10.1038/nrg1317},
pdf = {../local/Bredel2004Chemogenomics.pdf},
file = {Bredel2004Chemogenomics.pdf:Bredel2004Chemogenomics.pdf:PDF},
keywords = {chemogenomics},
owner = {vert},
pii = {nrg1317},
pmid = {15131650},
timestamp = {2007.08.02},
url = {http://dx.doi.org/10.1038/nrg1317}
}
@article{Caldwell1995introduction,
author = {Caldwell, J. and Gardner, I. and Swales, N.},
title = {An introduction to drug disposition: the basic principles of absorption,
distribution, metabolism, and excretion.},
journal = {Toxicol. Pathol.},
year = {1995},
volume = {23},
pages = {102--114},
number = {2},
abstract = {A knowledge of the fate of a drug, its disposition (absorption, distribution,
metabolism, and excretion, known by the acronym ADME) and pharmacokinetics
(the mathematical description of the rates of these processes and
of concentration-time relationships), plays a central role throughout
pharmaceutical research and development. These studies aid in the
discovery and selection of new chemical entities, support safety
assessment, and are critical in defining conditions for safe and
effective use in patients. ADME studies provide the only basis for
critical judgments from situations where the behavior of the drug
is understood to those where it is unknown: this is most important
in bridging from animal studies to the human situation. This presentation
is intended to provide an introductory overview of the life cycle
of a drug in the animal body and indicates the significance of such
information for a full understanding of mechanisms of action and
toxicity.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {7569663},
timestamp = {2008.07.16}
}
@article{Catapano2007G,
author = {Catapano, L. A. and Manji, H. K.},
title = {{G} protein-coupled receptors in major psychiatric disorders.},
journal = {Biochim. Biophys. Acta},
year = {2007},
volume = {1768},
pages = {976--993},
number = {4},
month = {Apr},
doi = {10.1016/j.bbamem.2006.09.025},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0005-2736(06)00384-1},
pmid = {17078926},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1016/j.bbamem.2006.09.025}
}
@article{Cavasotto2003Structure-based,
author = {Cavasotto, C. N. and Orry, A. J. W. and Abagyan, R. A.},
title = {Structure-based identification of binding sites, native ligands and
potential inhibitors for {G}-protein coupled receptors.},
journal = {Proteins},
year = {2003},
volume = {51},
pages = {423--433},
number = {3},
month = {May},
abstract = {G-protein coupled receptors (GPCRs) are the largest family of cell-surface
receptors involved in signal transmission. Drugs associated with
GPCRs represent more than one fourth of the 100 top-selling drugs
and are the targets of more than half of the current therapeutic
agents on the market. Our methodology based on the internal coordinate
mechanics (ICM) program can accurately identify the ligand-binding
pocket in the currently available crystal structures of seven transmembrane
(7TM) proteins [bacteriorhodopsin (BR) and bovine rhodopsin (bRho)].
The binding geometry of the ligand can be accurately predicted by
ICM flexible docking with and without the loop regions, a useful
finding for GPCR docking because the transmembrane regions are easier
to model. We also demonstrate that the native ligand can be identified
by flexible docking and scoring in 1.5\% and 0.2\% (for bRho and
BR, respectively) of the best scoring compounds from two different
types of compound database. The same procedure can be applied to
the database of available chemicals to identify specific GPCR binders.
Finally, we demonstrate that even if the sidechain positions in the
bRho binding pocket are entirely wrong, their correct conformation
can be fully restored with high accuracy (0.28 A) through the ICM
global optimization with and without the ligand present. These binding
site adjustments are critical for flexible docking of new ligands
to known structures or for docking to GPCR homology models. The ICM
docking method has the potential to be used to "de-orphanize" orphan
GPCRs (oGPCRs) and to identify antagonists-agonists for GPCRs if
an accurate model (experimentally and computationally validated)
of the structure has been constructed or when future crystal structures
are determined.},
doi = {10.1002/prot.10362},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12696053},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1002/prot.10362}
}
@article{Cavasotto2008Discovery,
author = {Cavasotto, C. N. and Orry, A. J. W. and Murgolo, N. J. and Czarniecki,
M. F. and Kocsi, S. A. and Hawes, B. E. and O'Neill, K. A. and Hine,
H. and Burton, M. S. and Voigt, J. H. and Abagyan, R. A. and Bayne,
M. L. and Monsma, F. J.},
title = {Discovery of novel chemotypes to a {G}-protein-coupled receptor through
ligand-steered homology modeling and structure-based virtual screening},
journal = {J. Med. Chem.},
year = {2008},
volume = {51},
pages = {581--588},
number = {3},
month = {Feb},
abstract = {Melanin-concentrating hormone receptor 1 (MCH-R1) is a G-protein-coupled
receptor (GPCR) and a target for the development of therapeutics
for obesity. The structure-based development of MCH-R1 and other
GPCR antagonists is hampered by the lack of an available experimentally
determined atomic structure. A ligand-steered homology modeling approach
has been developed (where information about existing ligands is used
explicitly to shape and optimize the binding site) followed by docking-based
virtual screening. Top scoring compounds identified virtually were
tested experimentally in an MCH-R1 competitive binding assay, and
six novel chemotypes as low micromolar affinity antagonist "hits"
were identified. This success rate is more than a 10-fold improvement
over random high-throughput screening, which supports our ligand-steered
method. Clearly, the ligand-steered homology modeling method reduces
the uncertainty of structure modeling for difficult targets like
GPCRs.},
doi = {10.1021/jm070759m},
keywords = {chemogenomics},
owner = {laurent},
pmid = {18198821},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/jm070759m}
}
@article{Chen2007GPCR,
author = {Chen, J.-Z. and Wang, J. and Xie, X.-Q.},
title = {GPCR structure-based virtual screening approach for CB2 antagonist
search.},
journal = {J. Chem. Inf. Model.},
year = {2007},
volume = {47},
pages = {1626--1637},
number = {4},
abstract = {The potential for therapeutic specificity in regulating diseases has
made cannabinoid (CB) receptors one of the most important G-protein-coupled
receptor (GPCR) targets in search for new drugs. Considering the
lack of related 3D experimental structures, we have established a
structure-based virtual screening protocol to search for CB2 bioactive
antagonists based on the 3D CB2 homology structure model. However,
the existing homology-predicted 3D models often deviate from the
native structure and therefore may incorrectly bias the in silico
design. To overcome this problem, we have developed a 3D testing
database query algorithm to examine the constructed 3D CB2 receptor
structure model as well as the predicted binding pocket. In the present
study, an antagonist-bound CB2 receptor complex model was initially
generated using flexible docking simulation and then further optimized
by molecular dynamic and mechanical (MD/MM) calculations. The refined
3D structural model of the CB2-ligand complex was then inspected
by exploring the interactions between the receptor and ligands in
order to predict the potential CB2 binding pocket for its antagonist.
The ligand-receptor complex model and the predicted antagonist binding
pockets were further processed and validated by FlexX-Pharm docking
against a testing compound database that contains known antagonists.
Furthermore, a consensus scoring (CScore) function algorithm was
established to rank the binding interaction modes of a ligand on
the CB2 receptor. Our results indicated that the known antagonists
seeded in the testing database can be distinguished from a significant
amount of randomly chosen molecules. Our studies demonstrated that
the established GPCR structure-based virtual screening approach provided
a new strategy with a high potential for in silico identifying novel
CB2 antagonist leads based on the homology-generated 3D CB2 structure
model.},
doi = {10.1021/ci7000814},
pdf = {../local/Chen2007GPCR.pdf},
file = {Chen2007GPCR.pdf:Chen2007GPCR.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {17580929},
timestamp = {2008.07.21},
url = {http://dx.doi.org/10.1021/ci7000814}
}
@article{Deupi2007Structural,
author = {Deupi, X. and D\"olker, N. and L\`opez-Rodr\`iguez, M. L. and Campillo,
M. and Ballesteros, J. A. and Pardo, L.},
title = {Structural models of class a {G} protein-coupled receptors as a tool
for drug design: insights on transmembrane bundle plasticity.},
journal = {Curr. Top. Med. Chem.},
year = {2007},
volume = {7},
pages = {991--998},
number = {10},
abstract = {G protein-coupled receptors (GPCRs) interact with an extraordinary
diversity of ligands by means of their extracellular domains and/or
the extracellular part of the transmembrane (TM) segments. Each receptor
subfamily has developed specific sequence motifs to adjust the structural
characteristics of its cognate ligands to a common set of conformational
rearrangements of the TM segments near the G protein binding domains
during the activation process. Thus, GPCRs have fulfilled this adaptation
during their evolution by customizing a preserved 7TM scaffold through
conformational plasticity. We use this term to describe the structural
differences near the binding site crevices among different receptor
subfamilies, responsible for the selective recognition of diverse
ligands among different receptor subfamilies. By comparing the sequence
of rhodopsin at specific key regions of the TM bundle with the sequences
of other GPCRs we have found that the extracellular region of TMs
2 and 3 provides a remarkable example of conformational plasticity
within Class A GPCRs. Thus, rhodopsin-based molecular models need
to include the plasticity of the binding sites among GPCR families,
since the "quality" of these homology models is intimately linked
with the success in the processes of rational drug-design or virtual
screening of chemical databases.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {17508932},
timestamp = {2008.07.21}
}
@article{Egan2000Prediction,
author = {Egan, W. J. and Merz, K. M. and Baldwin, J. J.},
title = {Prediction of drug absorption using multivariate statistics},
journal = {J. Med. Chem.},
year = {2000},
volume = {43},
pages = {3867--3877},
number = {21},
month = {Oct},
abstract = {Literature data on compounds both well- and poorly-absorbed in humans
were used to build a statistical pattern recognition model of passive
intestinal absorption. Robust outlier detection was utilized to analyze
the well-absorbed compounds, some of which were intermingled with
the poorly-absorbed compounds in the model space. Outliers were identified
as being actively transported. The descriptors chosen for inclusion
in the model were PSA and AlogP98, based on consideration of the
physical processes involved in membrane permeability and the interrelationships
and redundancies between available descriptors. These descriptors
are quite straightforward for a medicinal chemist to interpret, enhancing
the utility of the model. Molecular weight, while often used in passive
absorption models, was shown to be superfluous, as it is already
a component of both PSA and AlogP98. Extensive validation of the
model on hundreds of known orally delivered drugs, "drug-like" molecules,
and Pharmacopeia, Inc. compounds, which had been assayed for Caco-2
cell permeability, demonstrated a good rate of successful predictions
(74-92\%, depending on the dataset and exact criterion used).},
keywords = {chemogenomics},
owner = {laurent},
pii = {jm000292e},
pmid = {11052792},
timestamp = {2008.07.16}
}
@article{Erhan2006Collaborative,
author = {Erhan, D. and L'heureux, P.-J. and Yue, S. Y. and Bengio, Y.},
title = {Collaborative filtering on a family of biological targets.},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {626--635},
number = {2},
abstract = {Building a QSAR model of a new biological target for which few screening
data are available is a statistical challenge. However, the new target
may be part of a bigger family, for which we have more screening
data. Collaborative filtering or, more generally, multi-task learning,
is a machine learning approach that improves the generalization performance
of an algorithm by using information from related tasks as an inductive
bias. We use collaborative filtering techniques for building predictive
models that link multiple targets to multiple examples. The more
commonalities between the targets, the better the multi-target model
that can be built. We show an example of a multi-target neural network
that can use family information to produce a predictive model of
an undersampled target. We evaluate JRank, a kernel-based method
designed for collaborative filtering. We show their performance on
compound prioritization for an HTS campaign and the underlying shared
representation between targets. JRank outperformed the neural network
both in the single- and multi-target models.},
doi = {10.1021/ci050367t},
pdf = {../local/Erhan2006Collaborative.pdf},
file = {Erhan2006Collaborative.pdf:Erhan2006Collaborative.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16562992},
timestamp = {2007.10.11},
url = {http://dx.doi.org/10.1021/ci050367t}
}
@article{Evers2005Structure-based,
author = {Evers, A. and Klabunde, T.},
title = {Structure-based drug discovery using {GPCR} homology modeling: successful
virtual screening for antagonists of the {alpha1A} adrenergic receptor.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {1088--1097},
number = {4},
month = {Feb},
abstract = {In this paper, we describe homology modeling of the alpha1A receptor
based on the X-ray structure of bovine rhodopsin. The protein model
has been generated by applying ligand-supported homology modeling,
using mutational and ligand SAR data to guide the protein modeling
procedure. We performed a virtual screening of the company's compound
collection to test how well this model is suited to identify alpha1A
antagonists. We applied a hierarchical virtual screening procedure
guided by 2D filters and three-dimensional pharmacophore models.
The ca. 23,000 filtered compounds were docked into the alpha1A homology
model with GOLD and scored with PMF. From the top-ranked compounds,
80 diverse compounds were tested in a radioligand displacement assay.
37 compounds revealed K(i) values better than 10 microM; the most
active compound binds with 1.4 nM to the alpha1A receptor. Our findings
suggest that rhodopsin-based homology models may be used as the structural
basis for GPCR lead finding and compound optimization.},
doi = {10.1021/jm0491804},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15715476},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1021/jm0491804}
}
@article{Fredholm2007G-protein-coupled,
author = {Fredholm, B. B. and H{\"o}kfelt, T. and Milligan, G.},
title = {G-protein-coupled receptors: an update.},
journal = {Acta Physiol.},
year = {2007},
volume = {190},
pages = {3--7},
number = {1},
month = {May},
abstract = {The receptors that couple to G proteins (GPCR) and which span the
cell membranes seven times (7-TM receptors) were the focus of a symposium
in Stockholm 2006. The ensemble of GPCR has now been mapped in several
animal species. They remain a major focus of interest in drug development,
and their diverse physiological and pathophysiological roles are
being clarified, i.a. by genetic targeting. Recent developments hint
at novel levels of complexity. First, many, if not all, GPCRs are
part of multimeric ensembles, and physiology and pharmacology of
a given GPCR may be at least partly guided by the partners it was
formed together with. Secondly, at least some GPCRs may be constitutively
active. Therefore, drugs that are inverse agonists may prove useful.
Furthermore, the level of activity may vary in such a profound way
between cells and tissues that this could offer new ways of achieving
specificity of drug action. Finally, it is becoming increasingly
clear that some of these receptors can signal via novel types of
pathways, and hence that 'GPCRs' may not always be G-protein-coupled.
Thus there are many challenges for the basic scientist and the drug
industry.},
doi = {10.1111/j.1365-201X.2007.01689.x},
keywords = {chemogenomics},
owner = {laurent},
pii = {APS1689},
pmid = {17428227},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1111/j.1365-201X.2007.01689.x}
}
@article{Freyhult2005Unbiased,
author = {Freyhult, E. and Prusis, P. and Lapinsh, M. and Wikberg, J. E. S.
and Moulton, V. and Gustafsson, M. G.},
title = {Unbiased descriptor and parameter selection confirms the potential
of proteochemometric modelling.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {50},
abstract = {BACKGROUND: Proteochemometrics is a new methodology that allows prediction
of protein function directly from real interaction measurement data
without the need of 3D structure information. Several reported proteochemometric
models of ligand-receptor interactions have already yielded significant
insights into various forms of bio-molecular interactions. The proteochemometric
models are multivariate regression models that predict binding affinity
for a particular combination of features of the ligand and protein.
Although proteochemometric models have already offered interesting
results in various studies, no detailed statistical evaluation of
their average predictive power has been performed. In particular,
variable subset selection performed to date has always relied on
using all available examples, a situation also encountered in microarray
gene expression data analysis. RESULTS: A methodology for an unbiased
evaluation of the predictive power of proteochemometric models was
implemented and results from applying it to two of the largest proteochemometric
data sets yet reported are presented. A double cross-validation loop
procedure is used to estimate the expected performance of a given
design method. The unbiased performance estimates (P2) obtained for
the data sets that we consider confirm that properly designed single
proteochemometric models have useful predictive power, but that a
standard design based on cross validation may yield models with quite
limited performance. The results also show that different commercial
software packages employed for the design of proteochemometric models
may yield very different and therefore misleading performance estimates.
In addition, the differences in the models obtained in the double
CV loop indicate that detailed chemical interpretation of a single
proteochemometric model is uncertain when data sets are small. CONCLUSION:
The double CV loop employed offer unbiased performance estimates
about a given proteochemometric modelling procedure, making it possible
to identify cases where the proteochemometric design does not result
in useful predictive models. Chemical interpretations of single proteochemometric
models are uncertain and should instead be based on all the models
selected in the double CV loop employed here.},
doi = {10.1186/1471-2105-6-50},
keywords = {chemogenomics},
owner = {laurent},
pii = {1471-2105-6-50},
pmid = {15760465},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1186/1471-2105-6-50}
}
@article{Frimurer2005physicogenetic,
author = {Frimurer, T. M. and Ulven, T. and Elling, C. E. and Gerlach, L.-O.
and Kostenis, E. and H{\"o}gberg, T.},
title = {A physicogenetic method to assign ligand-binding relationships between
7TM receptors.},
journal = {Bioorg. Med. Chem. Lett.},
year = {2005},
volume = {15},
pages = {3707--3712},
number = {16},
month = {Aug},
abstract = {A computational protocol has been devised to relate 7TM receptor proteins
(GPCRs) with respect to physicochemical features of the core ligand-binding
site as defined from the crystal structure of bovine rhodopsin. The
identification of such receptors that already are associated with
ligand information (e.g., small molecule ligands with mutagenesis
or SAR data) is used to support structure-guided drug design of novel
ligands. A case targeting the newly identified prostaglandin D2 receptor
CRTH2 serves as a primary example to illustrate the procedure.},
doi = {10.1016/j.bmcl.2005.05.102},
pdf = {../local/Frimurer2005physicogenetic.pdf},
file = {Frimurer2005physicogenetic.pdf:Frimurer2005physicogenetic.pdf:PDF},
keywords = {chemogenomics},
owner = {vert},
pii = {S0960-894X(05)00704-3},
pmid = {15993056},
timestamp = {2007.12.12},
url = {http://dx.doi.org/10.1016/j.bmcl.2005.05.102}
}
@article{Guba2006Chemogenomics,
author = {Guba, W.},
title = {Chemogenomics strategies for G-protein coupled receptor hit finding.},
journal = {Ernst Schering Res Found Workshop},
year = {2006},
volume = {58},
pages = {21--29},
abstract = {Targeting protein superfamilies via chemogenomics is based on a similarity
clustering of gene sequences and molecular structures of ligands.
Both target and ligand clusters are linked by generating binding
affinity profiles of chemotypes vs a target panel. The application
of this multidimensional similarity paradigm will be described in
the context of Lead Generation to identify novel chemical hit classes
for G-protein coupled receptors.},
doi = {10.1007/3-540-37635-6_2},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16708996},
timestamp = {2007.07.30}
}
@article{Hill2006G-protein-coupled,
author = {Hill, S. J.},
title = {{G}-protein-coupled receptors: past, present and future.},
journal = {Br. J. Pharmacol.},
year = {2006},
volume = {147 Suppl 1},
pages = {S27--S37},
month = {Jan},
abstract = {The G-protein-coupled receptor (GPCR) family represents the largest
and most versatile group of cell surface receptors. Drugs active
at these receptors have therapeutic actions across a wide range of
human diseases ranging from allergic rhinitis to pain, hypertension
and schizophrenia. This review provides a brief historical overview
of the properties and signalling characteristics of this important
family of receptors.},
doi = {10.1038/sj.bjp.0706455},
keywords = {chemogenomics},
owner = {laurent},
pii = {0706455},
pmid = {16402114},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1038/sj.bjp.0706455}
}
@article{Hopkins2002druggable,
author = {Hopkins, A. L. and Groom, C. R.},
title = {The druggable genome},
journal = {Nat. Rev. Drug Discov.},
year = {2002},
volume = {1},
pages = {727--730},
number = {9},
month = {Sep},
abstract = {An assessment of the number of molecular targets that represent an
opportunity for therapeutic intervention is crucial to the development
of post-genomic research strategies within the pharmaceutical industry.
Now that we know the size of the human genome, it is interesting
to consider just how many molecular targets this opportunity represents.
We start from the position that we understand the properties that
are required for a good drug, and therefore must be able to understand
what makes a good drug target.},
doi = {10.1038/nrd892},
keywords = {chemogenomics},
owner = {laurent},
pii = {nrd892},
pmid = {12209152},
timestamp = {2007.09.22},
url = {http://dx.doi.org/10.1038/nrd892}
}
@article{Horn2003GPCRDB,
author = {Horn, F. and Bettler, E. and Oliveira, L. and Campagne, F. and Cohen,
F. E. and Vriend, G.},
title = {{GPCRDB} information system for {G} protein-coupled receptors},
journal = {Nucl. Acids Res.},
year = {2003},
volume = {31},
pages = {294-297},
number = {1},
abstract = {The GPCRDB is a molecular class-specific information system that collects,
combines, validates and disseminates heterogeneous data on G protein-coupled
receptors (GPCRs). The database stores data on sequences, ligand
binding constants and mutations. The system also provides computationally
derived data such as sequence alignments, homology models, and a
series of query and visualization tools. The GPCRDB is updated automatically
once every 4-5 months and is freely accessible at http://www.gpcr.org/7tm/.},
doi = {10.1093/nar/gkg103},
eprint = {http://nar.oxfordjournals.org/cgi/reprint/31/1/294.pdf},
keywords = {chemogenomics},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/31/1/294}
}
@article{Jacob2008Virtual,
author = {Jacob, L. and Hoffmann, B. and Stoven, V. and Vert, J.-P.},
title = {Virtual screening of {GPCR}s: an {\it in silico} chemogenomics approach},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {363},
doi = {10.1186/1471-2105-9-363},
pdf = {../local/Jacob2008Virtual.pdf},
file = {Jacob2008Virtual.pdf:Jacob2008Virtual.pdf:PDF},
keywords = {chemogenomics},
url = {http://dx.doi.org/10.1186/1471-2105-9-363}
}
@article{Jacob2008Efficient,
author = {Jacob, L. and Vert, J.-P.},
title = {Efficient peptide-{MHC}-{I} binding prediction for alleles with few
known binders.},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {358--366},
number = {3},
month = {Feb},
abstract = {MOTIVATION: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role
in the identification of T-cell epitopes. Statistical and machine
learning methods in particular are widely used to score candidate
binders based on their similarity with known binders and non-binders.
The genes coding for the MHC molecules, however, are highly polymorphic,
and statistical methods have difficulties building models for alleles
with few known binders. In this context, recent work has demonstrated
the utility of leveraging information across alleles to improve the
performance of the prediction. RESULTS: We design a support vector
machine algorithm that is able to learn peptide-MHC-I binding models
for many alleles simultaneously, by sharing binding information across
alleles. The sharing of information is controlled by a user-defined
measure of similarity between alleles. We show that this similarity
can be defined in terms of supertypes, or more directly by comparing
key residues known to play a role in the peptide-MHC binding. We
illustrate the potential of this approach on various benchmark experiments
where it outperforms other state-of-the-art methods. AVAILABILITY:
The method is implemented on a web server: http://cbio.ensmp.fr/kiss.
All data and codes are freely and publicly available from the authors.},
doi = {10.1093/bioinformatics/btm611},
pdf = {../local/Jacob2008Efficient.pdf},
file = {Jacob2008Efficient.pdf:Jacob2008Efficient.pdf:PDF},
keywords = {chemogenomics immunoinformatics},
owner = {laurent},
pii = {btm611},
pmid = {18083718},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btm611}
}
@article{Jacob2008Protein,
author = {Jacob, L. and Vert, J.-P.},
title = {Protein-ligand interaction prediction: an improved chemogenomics
approach},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {2149--2156},
number = {19},
doi = {10.1093/bioinformatics/btn409},
pdf = {../local/Jacob2008Protein.pdf},
file = {Jacob2008Protein.pdf:Jacob2008Protein.pdf:PDF},
keywords = {chemogenomics},
url = {http://bioinformatics.oxfordjournals.org/cgi/reprint/btn409}
}
@techreport{Jacob2007Kernel,
author = {Jacob, L. and Vert, J.-P.},
title = {Kernel methods for in silico chemogenomics},
institution = {arXiv},
year = {2007},
number = {0709.3931v1},
keywords = {chemogenomics},
timestamp = {2007.10.25},
url = {http://fr.arxiv.org/abs/0709.3931}
}
@article{Kellenberger2008How,
author = {Kellenberger, E. and Schalon, C. and Rognan, D.},
title = {How to Measure the Similarity Between Protein Ligand-Binding Sites?},
journal = {Current Computer-Aided Drug Design},
year = {2008},
volume = {4},
pages = {209--220},
number = {3},
month = {Sep.},
abstract = {Quantification of local similarity between protein 3D structures is
a promising tool in computer-aided drug design and prediction of
biological function. Over the last ten years, several computational
methods were proposed, mostly based on geometrical comparisons. This
review summarizes the recent literature and gives an overview of
available programs.
A particular interest is given to the underlying methodologies. Our
analysis points out strengths and weaknesses of the various approaches.
If all described methods work relatively well when two binding sites
obviously resemble each other, scoring potential solutions remains
a difficult issue, especially if the similarity is low. The other
challenging question is the protein flexibility, which is indeed
difficult to evaluate from a static representation. Last, most of
recently developed techniques are fast and can be applied to large
amounts of data.
Examples were carefully chosen to illustrate the wide applicability
domain of the most popular methods: detection of common structural
motifs, identification of secondary targets for a drug-like compound,
comparison of binding sites across a functional family, comparison
of homology models, database screening.},
doi = {10.2174/157340908785747401},
pdf = {../local/Kellenberger2008How.pdf},
file = {Kellenberger2008How.pdf:Kellenberger2008How.pdf:PDF},
keywords = {chemogenomics},
owner = {jp},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.2174/157340908785747401}
}
@article{Klabunde2007Chemogenomic,
author = {Klabunde, T.},
title = {Chemogenomic approaches to drug discovery: similar receptors bind
similar ligands.},
journal = {Br. J. Pharmacol.},
year = {2007},
volume = {152},
pages = {5--7},
month = {May},
abstract = {Within recent years, a paradigm shift from traditional receptor-specific
studies to a cross-receptor view has taken place within pharmaceutical
research to increase the efficiency of modern drug discovery. Receptors
are no longer viewed as single entities but grouped into sets of
related proteins or receptor families that are explored in a systematic
manner. This interdisciplinary approach attempting to derive predictive
links between the chemical structures of bioactive molecules and
the receptors with which these molecules interact is referred to
as chemogenomics. Insights from chemogenomics are used for the rational
compilation of screening sets and for the rational design and synthesis
of directed chemical libraries to accelerate drug discovery.British
Journal of Pharmacology advance online publication, 29 May 2007;
doi:10.1038/sj.bjp.0707308.},
doi = {10.1038/sj.bjp.0707308},
keywords = {chemogenomics},
owner = {laurent},
pii = {0707308},
pmid = {17533415},
timestamp = {2007.07.30},
url = {http://dx.doi.org/10.1038/sj.bjp.0707308}
}
@article{Klabunde2006Chemogenomics,
author = {T. Klabunde and R. J{\"a}ger},
title = {Chemogenomics approaches to G-protein coupled receptor lead finding.},
journal = {Ernst Schering Res Found Workshop},
year = {2006},
volume = {58},
pages = {31--46},
abstract = {G-protein coupled receptors (GPCRs) are promising targets for the
discovery of novel drugs. In order to identify novel chemical series,
high-throughput screening (HTS) is often complemented by rational
chemogenomics lead finding approaches. We have compiled a GPCR directed
screening set by ligand-based virtual screening of our corporate
compound database. This set of compounds is supplemented with novel
libraries synthesized around proprietary scaffolds. These target-directed
libraries are designed using the knowledge of privileged fragments
and pharmacophores to address specific GPCR subfamilies (e.g., purinergic
or chemokine-binding GPCRs). Experimental testing of the GPCR collection
has provided novel chemical series for several GPCR targets including
the adenosine A1, the P2Y12, and the chemokine CCR1 receptor. In
addition, GPCR sequence motifs linked to the recognition of GPCR
ligands (termed chemoprints) are identified using homology modeling,
molecular docking, and experimental profiling. These chemoprints
can support the design and synthesis of compound libraries tailor-made
for a novel GPCR target.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16708997},
timestamp = {2007.07.30}
}
@article{Kobilka2007G,
author = {Kobilka, B. K.},
title = {G protein coupled receptor structure and activation.},
journal = {Biochim. Biophys. Acta},
year = {2007},
volume = {1768},
pages = {794--807},
number = {4},
month = {Apr},
abstract = {G protein coupled receptors (GPCRs) are remarkably versatile signaling
molecules. The members of this large family of membrane proteins
are activated by a spectrum of structurally diverse ligands, and
have been shown to modulate the activity of different signaling pathways
in a ligand specific manner. In this manuscript I will review what
is known about the structure and mechanism of activation of GPCRs
focusing primarily on two model systems, rhodopsin and the beta(2)
adrenoceptor.},
doi = {10.1016/j.bbamem.2006.10.021},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0005-2736(06)00398-1},
pmid = {17188232},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1016/j.bbamem.2006.10.021}
}
@article{Kratochwil2005automated,
author = {Kratochwil, N. A. and Malherbe, P. and Lindemann, L. and Ebeling,
M. and Hoener, M. C. and M{\"u}hlemann, A. and Porter, R. H. P. and
Stahl, M. and Gerber, P. R.},
title = {An automated system for the analysis of {G} protein-coupled receptor
transmembrane binding pockets: alignment, receptor-based pharmacophores,
and their application.},
journal = {J. Chem. Inf. Model.},
year = {2005},
volume = {45},
pages = {1324--1336},
number = {5},
abstract = {G protein-coupled receptors (GPCRs) share a common architecture consisting
of seven transmembrane (TM) domains. Various lines of evidence suggest
that this fold provides a generic binding pocket within the TM region
for hosting agonists, antagonists, and allosteric modulators. Here,
a comprehensive and automated method allowing fast analysis and comparison
of these putative binding pockets across the entire GPCR family is
presented. The method relies on a robust alignment algorithm based
on conservation indices, focusing on pharmacophore-like relationships
between amino acids. Analysis of conservation patterns across the
GPCR family and alignment to the rhodopsin X-ray structure allows
the extraction of the amino acids lining the TM binding pocket in
a so-called ligand binding pocket vector (LPV). In a second step,
LPVs are translated to simple 3D receptor pharmacophore models, where
each amino acid is represented by a single spherical pharmacophore
feature and all atomic detail is omitted. Applications of the method
include the assessment of selectivity issues, support of mutagenesis
studies, and the derivation of rules for focused screening to identify
chemical starting points in early drug discovery projects. Because
of the coarseness of this 3D receptor pharmacophore model, however,
meaningful scoring and ranking procedures of large sets of molecules
are not justified. The LPV analysis of the trace amine-associated
receptor family and its experimental validation is discussed as an
example. The value of the 3D receptor model is demonstrated for a
class C GPCR family, the metabotropic glutamate receptors.},
doi = {10.1021/ci050221u},
pdf = {../local/Kratochwil2005automated.pdf},
file = {Kratochwil2005automated.pdf:Kratochwil2005automated.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16180909},
timestamp = {2007.09.22},
url = {http://dx.doi.org/10.1021/ci050221u}
}
@article{Lapinsh2005Improved,
author = {Lapinsh, M. and Prusis, P. and Uhl{\'e}n, S. and Wikberg, J. E. S.},
title = {Improved approach for proteochemometrics modeling: application to
organic compound--amine {G} protein-coupled receptor interactions.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {4289--4296},
number = {23},
month = {Dec},
abstract = {MOTIVATION: Proteochemometrics is a novel technology for the analysis
of interactions of series of proteins with series of ligands. We
have here customized it for analysis of large datasets and evaluated
it for the modeling of the interaction of psychoactive organic amines
with all the five known families of amine G protein-coupled receptors
(GPCRs). RESULTS: The model exploited data for the binding of 22
compounds to 31 amine GPCRs, correlating chemical descriptions and
cross-descriptions of compounds and receptors to binding affinity
using a novel strategy. A highly valid model (q2 = 0.76) was obtained
which was further validated by external predictions using data for
10 other entirely independent compounds, yielding the high q2ext
= 0.67. Interpretation of the model reveals molecular interactions
that govern psychoactive organic amines overall affinity for amine
GPCRs, as well as their selectivity for particular amine GPCRs. The
new modeling procedure allows us to obtain fully interpretable proteochemometrics
models using essentially unlimited number of ligand and protein descriptors.},
doi = {10.1093/bioinformatics/bti703},
keywords = {chemogenomics},
owner = {laurent},
pii = {bti703},
pmid = {16204343},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1093/bioinformatics/bti703}
}
@article{Lefkowitz2008crystal,
author = {Lefkowitz, R. J. and Sun, J.-P. and Shukla, A. K.},
title = {A crystal clear view of the beta2-adrenergic receptor},
journal = {Nat. Biotechnol.},
year = {2008},
volume = {26},
pages = {189--191},
number = {2},
month = {Feb},
doi = {10.1038/nbt0208-189},
keywords = {chemogenomics},
owner = {laurent},
pii = {nbt0208-189},
pmid = {18259173},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1038/nbt0208-189}
}
@article{Lin2004Orphan,
author = {Lin, S. H. S. and Civelli, O.},
title = {Orphan {G} protein-coupled receptors: targets for new therapeutic
interventions.},
journal = {Ann. Med.},
year = {2004},
volume = {36},
pages = {204--214},
number = {3},
abstract = {With the completion of the human genome, many genes will be uncovered
with unknown functions. The 'orphan' G protein coupled receptors
(GPCRs) are examples of genes without known functions. These are
genes that exhibit the seven helical conformation hallmark of the
GPCRs but that are called 'orphans' because they are activated by
none of the primary messengers known to activate GPCRs in vivo. They
are the targets of undiscovered transmitters and this lack of knowledge
precludes understanding their function. Yet, because they belong
to the supergene family that has the widest regulatory role in the
organism, the orphan GPCRs have generated much excitement in academia
and industry. They hold much hope for revealing new intercellular
interactions that will open new areas of basic research which ultimately
will lead to new therapeutic applications. However, the first step
in understanding the function of orphan GPCRs is to 'deorphanize'
them, to identify their natural transmitters. Here we review the
search for the natural primary messengers of orphan GPCRs and focus
on two recently deorphanized GPCR systems, the melanin-concentrating
hormone (MCH) and prolactin-releasing peptide (PrRP) systems, to
illustrate the strategies applied to solve their function and to
exemplify the therapeutic potentials that such systems hold.},
doi = {10.1080/07853890310024668},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15181976},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1080/07853890310024668}
}
@article{Martin2005bioavailability,
author = {Martin, Y. C.},
title = {A bioavailability score},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {3164--3170},
number = {9},
month = {May},
abstract = {Responding to a demonstrated need for scientists to forecast the permeability
and bioavailability (F) properties of compounds before their purchase,
synthesis, or advanced testing, we have developed a score that assigns
the probability that a compound will have F > 10\% in the rat. Neither
the rule-of-five, log P, log D, nor the combination of the number
of rotatable bonds and polar surface area successfully categorized
compounds. Instead, different properties govern the bioavailability
of compounds depending on their predominant charge at biological
pH. The fraction of anions with >10\% F falls from 85\% if the polar
surface area (PSA) is < or = 75 A(2), to 56\% if 75 < PSA < 150 A(2),
to 11\% if PSA is > or = 150 A(2). On the other hand, whereas 55\%
of the neutral, zwitterionic, or cationic compounds that pass the
rule-of-five have >10\% F, only 17\% of those that fail have > 10\%
F. This same categorization distinguishes compounds that are poorly
permeable from those that are permeable in Caco-2 cells. Further
validation is provided with human bioavailability values from the
literature.},
doi = {10.1021/jm0492002},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15857122},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/jm0492002}
}
@article{Mirzadegan2003Sequence,
author = {Mirzadegan, T. and Benk{\"o}, G. and Filipek, S. and Palczewski,
K.},
title = {Sequence analyses of {G}-protein-coupled receptors: similarities
to rhodopsin},
journal = {Biochemistry},
year = {2003},
volume = {42},
pages = {2759--2767},
number = {10},
month = {Mar},
doi = {10.1021/bi027224+},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12627940},
timestamp = {2008.07.16}
}
@article{Okada2004retinal,
author = {Okada, T. and Sugihara, M. and Bondar, A.-N. and Elstner, M. and
Entel, P. and Buss, V.},
title = {The retinal conformation and its environment in rhodopsin in light
of a new 2.2 A crystal structure.},
journal = {J. Mol. Biol.},
year = {2004},
volume = {342},
pages = {571--583},
number = {2},
month = {Sep},
abstract = {A new high-resolution structure is reported for bovine rhodopsin,
the visual pigment in rod photoreceptor cells. Substantial improvement
of the resolution limit to 2.2 A has been achieved by new crystallization
conditions, which also reduce significantly the probability of merohedral
twinning in the crystals. The new structure completely resolves the
polypeptide chain and provides further details of the chromophore
binding site including the configuration about the C6-C7 single bond
of the 11-cis-retinal Schiff base. Based on both an earlier structure
and the new improved model of the protein, a theoretical study of
the chromophore geometry has been carried out using combined quantum
mechanics/force field molecular dynamics. The consistency between
the experimental and calculated chromophore structures is found to
be significantly improved for the 2.2 A model, including the angle
of the negatively twisted 6-s-cis-bond. Importantly, the new crystal
structure refinement reveals significant negative pre-twist of the
C11-C12 double bond and this is also supported by the theoretical
calculation although the latter converges to a smaller value. Bond
alternation along the unsaturated chain is significant, but weaker
in the calculated structure than the one obtained from the X-ray
data. Other differences between the experimental and theoretical
structures in the chromophore binding site are discussed with respect
to the unique spectral properties and excited state reactivity of
the chromophore.},
doi = {10.1016/j.jmb.2004.07.044},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0022-2836(04)00873-3},
pmid = {15327956},
timestamp = {2008.04.01},
url = {http://dx.doi.org/10.1016/j.jmb.2004.07.044}
}
@article{Okuno2007GLIDA,
author = {Okuno, Y. and Tamon, A. and Yabuuchi, H. and Niijima, S. and Minowa,
Y. and Tonomura, K. and Kunimoto, R. and Feng, C.},
title = {{GLIDA}: {GPCR} ligand database for chemical genomics drug discovery
database and tools update.},
journal = {Nucleic Acids Res.},
year = {2007},
volume = {36},
pages = {D907--D912},
number = {Database issue},
month = {Nov},
abstract = {G-protein coupled receptors (GPCRs) represent one of the most important
families of drug targets in pharmaceutical development. GLIDA is
a public GPCR-related Chemical Genomics database that is primarily
focused on the integration of information between GPCRs and their
ligands. It provides interaction data between GPCRs and their ligands,
along with chemical information on the ligands, as well as biological
information regarding GPCRs. These data are connected with each other
in a relational database, allowing users in the field of Chemical
Genomics research to easily retrieve such information from either
biological or chemical starting points. GLIDA includes a variety
of similarity search functions for the GPCRs and for their ligands.
Thus, GLIDA can provide correlation maps linking the searched homologous
GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the
correlation patterns between GPCRs and ligands, we can gain more
detailed knowledge about their conserved molecular recognition patterns
and improve drug design efforts by focusing on inferred candidates
for GPCR-specific drugs. This article provides a summary of the GLIDA
database and user facilities, and describes recent improvements to
database design, data contents, ligand classification programs, similarity
search options and graphical interfaces. GLIDA is publicly available
at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope
that it will prove very useful for Chemical Genomics research and
GPCR-related drug discovery.},
doi = {10.1093/nar/gkm948},
keywords = {chemogenomics},
owner = {laurent},
pii = {gkm948},
pmid = {17986454},
timestamp = {2008.01.15},
url = {http://dx.doi.org/10.1093/nar/gkm948}
}
@article{Okuno2006GLIDA,
author = {Okuno, Y. and Yang, J. and Taneishi, K. and Yabuuchi, H. and Tsujimoto,
G.},
title = {{GLIDA}: {GPCR}-ligand database for chemical genomic drug discovery},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {D673--D677},
number = {Database issue},
month = {Jan},
abstract = {G-protein coupled receptors (GPCRs) represent one of the most important
families of drug targets in pharmaceutical development. GPCR-LIgand
DAtabase (GLIDA) is a novel public GPCR-related chemical genomic
database that is primarily focused on the correlation of information
between GPCRs and their ligands. It provides correlation data between
GPCRs and their ligands, along with chemical information on the ligands,
as well as access information to the various web databases regarding
GPCRs. These data are connected with each other in a relational database,
allowing users in the field of GPCR-related drug discovery to easily
retrieve such information from either biological or chemical starting
points. GLIDA includes structure similarity search functions for
the GPCRs and for their ligands. Thus, GLIDA can provide correlation
maps linking the searched homologous GPCRs (or ligands) with their
ligands (or GPCRs). By analyzing the correlation patterns between
GPCRs and ligands, we can gain more detailed knowledge about their
interactions and improve drug design efforts by focusing on inferred
candidates for GPCR-specific drugs. GLIDA is publicly available at
http://gdds.pharm.kyoto-u.ac.jp:8081/glida. We hope that it will
prove very useful for chemical genomic research and GPCR-related
drug discovery.},
doi = {10.1093/nar/gkj028},
keywords = {chemogenomics},
owner = {laurent},
pii = {34/suppl_1/D673},
pmid = {16381956},
timestamp = {2008.01.15},
url = {http://dx.doi.org/10.1093/nar/gkj028}
}
@article{Perlman2004Multidimensional,
author = {Perlman, Z. E. and Slack, M. D. and Feng, Y. and Mitchison, T. J.
and Wu, L. F. and Altschuler, S. J.},
title = {Multidimensional drug profiling by automated microscopy},
journal = {Science},
year = {2004},
volume = {306},
pages = {1194--1198},
number = {5699},
month = {Nov},
abstract = {We present a method for high-throughput cytological profiling by microscopy.
Our system provides quantitative multidimensional measures of individual
cell states over wide ranges of perturbations. We profile dose-dependent
phenotypic effects of drugs in human cell culture with a titration-invariant
similarity score (TISS). This method successfully categorized blinded
drugs and suggested targets for drugs of uncertain mechanism. Multivariate
single-cell analysis is a starting point for identifying relationships
among drug effects at a systems level and a step toward phenotypic
profiling at the single-cell level. Our methods will be useful for
discovering the mechanism and predicting the toxicity of new drugs.},
doi = {10.1126/science.1100709},
pdf = {../local/Perlman2004Multidimensional.pdf},
file = {Perlman2004Multidimensional.pdf:Perlman2004Multidimensional.pdf:PDF},
institution = {Institute of Chemistry and Cell Biology, Harvard Medical School,
Boston, MA 02115, USA.},
keywords = {chemogenomics, highcontentscreening},
owner = {jp},
pii = {306/5699/1194},
pmid = {15539606},
timestamp = {2009.03.26},
url = {http://dx.doi.org/10.1126/science.1100709}
}
@article{Rolland2005G-protein-coupled,
author = {Rolland, C. and Gozalbes, R. and Nicola{\"i}, A. and Paugam, M.-F.
and Coussy, L. and Barbosa, F. and Horvath, D. and Revah, F.},
title = {G-protein-coupled receptor affinity prediction based on the use of
a profiling dataset: QSAR design, synthesis, and experimental validation.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {6563--6574},
number = {21},
month = {Oct},
abstract = {A QSAR model accounting for "average" G-protein-coupled receptor (GPCR)
binding was built from a large set of experimental standardized binding
data (1939 compounds systematically tested over 40 different GPCRs)
and applied to the design of a library of "GPCR-predicted" compounds.
Three hundred and sixty of these compounds were randomly selected
and tested in 21 GPCR binding assays. Positives were defined by their
ability to inhibit by more than 70\% the binding of reference compounds
at 10 microM. A 5.5-fold enrichment in positives was observed when
comparing the "GPCR-predicted" compounds with 600 randomly selected
compounds predicted as "non-GPCR" from a general collection. The
model was efficient in predicting strongest binders, since enrichment
was greater for higher cutoffs. Significant enrichment was also observed
for peptidic GPCRs and receptors not included to develop the QSAR
model, suggesting the usefulness of the model to design ligands binding
with newly identified GPCRs, including orphan ones.},
doi = {10.1021/jm0500673},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16220973},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1021/jm0500673}
}
@article{Schuffenhauer2003Similarity,
author = {Schuffenhauer, A. and Floersheim, P. and Acklin, P. and Jacoby, E.},
title = {Similarity metrics for ligands reflecting the similarity of the target
proteins},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {391--405},
number = {2},
abstract = {In this study we evaluate how far the scope of similarity searching
can be extended to identify not only ligands binding to the same
target as the reference ligand(s) but also ligands of other homologous
targets without initially known ligands. This "homology-based similarity
searching" requires molecular representations reflecting the ability
of a molecule to interact with target proteins. The Similog keys,
which are introduced here as a new molecular representation, were
designed to fulfill such requirements. They are based only on the
molecular constitution and are counts of atom triplets. Each triplet
is characterized by the graph distances and the types of its atoms.
The atom-typing scheme classifies each atom by its function as H-bond
donor or acceptor and by its electronegativity and bulkiness. In
this study the Similog keys are investigated in retrospective in
silico screening experiments and compared with other conformation
independent molecular representations. Studied were molecules of
the MDDR database for which the activity data was augmented by standardized
target classification information from public protein classification
databases. The MDDR molecule set was split randomly into two halves.
The first half formed the candidate set. Ligands of four targets
(dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease,
and progesterone receptor) were taken from the second half to form
the respective reference sets. Different similarity calculation methods
are used to rank the molecules of the candidate set by their similarity
to each of the four reference sets. The accumulated counts of molecules
binding to the reference target and groups of targets with decreasing
homology to it were examined as a function of the similarity rank
for each reference set and similarity method. In summary, similarity
searching based on Unity 2D-fingerprints or Similog keys are found
to be equally effective in the identification of molecules binding
to the same target as the reference set. However, the application
of the Similog keys is more effective in comparison with the other
investigated methods in the identification of ligands binding to
any target belonging to the same family as the reference target.
We attribute this superiority to the fact that the Similog keys provide
a generalization of the chemical elements and that the keys are counted
instead of merely noting their presence or absence in a binary form.
The second most effective molecular representation are the occurrence
counts of the public ISIS key fragments, which like the Similog method,
incorporates key counting as well as a generalization of the chemical
elements. The results obtained suggest that ligands for a new target
can be identified by the following three-step procedure: 1. Select
at least one target with known ligands which is homologous to the
new target. 2. Combine the known ligands of the selected target(s)
to a reference set. 3. Search candidate ligands for the new targets
by their similarity to the reference set using the Similog method.
This clearly enlarges the scope of similarity searching from the
classical application for a single target to the identification of
candidate ligands for whole target families and is expected to be
of key utility for further systematic chemogenomics exploration of
previously well explored target families.},
doi = {10.1021/ci025569t},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12653501},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/ci025569t}
}
@article{Schuffenhauer2002ontology,
author = {Schuffenhauer, A. and Zimmermann, J. and Stoop, R. and van der Vyver,
J. J. and Lecchini, S. and Jacoby, E.},
title = {An ontology for pharmaceutical ligands and its application for in
silico screening and library design},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2002},
volume = {42},
pages = {947--955},
number = {4},
abstract = {Annotation efforts in biosciences have focused in past years mainly
on the annotation of genomic sequences. Only very limited effort
has been put into annotation schemes for pharmaceutical ligands.
Here we propose annotation schemes for the ligands of four major
target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear
receptors (NRs), and ligand-gated ion channels (LGICs), and outline
their usage for in silico screening and combinatorial library design.
The proposed schemes cover ligand functionality and hierarchical
levels of target classification. The classification schemes are based
on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The
ligands of the MDL Drug Data Report (MDDR) database serve as a reference
data set of known pharmacologically active compounds. All ligands
were annotated according to the schemes when attribution was possible
based on the activity classification provided by the reference database.
The purpose of the ligand-target classification schemes is to allow
annotation-based searching of the ligand database. In addition, the
biological sequence information of the target is directly linkable
to the ligand, hereby allowing sequence similarity-based identification
of ligands of next homologous receptors. Ligands of specified levels
can easily be retrieved to serve as comprehensive reference sets
for cheminformatics-based similarity searches and for design of target
class focused compound libraries. Retrospective in silico screening
experiments within the MDDR01.1 database, searching for structures
binding to dopamine D2, all dopamine receptors and all amine-binding
class A GPCRs using known dopamine D2 binding compounds as a reference
set, have shown that such reference sets are in particular useful
for the identification of ligands binding to receptors closely related
to the reference system. The potential for ligand identification
drops with increasing phylogenetic distance. The analysis of the
focus of a tertiary amine based combinatorial library compared to
known amine binding class A GPCRs, peptide binding class A GPCRs,
and LGIC ligands constitutes a second application scenario which
illustrates how the focus of a combinatorial library can be treated
quantitatively. The provided annotation schemes, which bridge chem-
and bioinformatics by linking ligands to sequences, are expected
to be of key utility for further systematic chemogenomics exploration
of previously well explored target families.},
keywords = {chemogenomics},
owner = {laurent},
pii = {ci010385k},
pmid = {12132896},
timestamp = {2008.07.16}
}
@article{Shacham2004PREDICT,
author = {Shacham, S. and Marantz, Y. and Bar-Haim, S. and Kalid, O. and Warshaviak,
D. and Avisar, N. and Inbal, B. and Heifetz, A. and Fichman, M. and
Topf, M. and Naor, Z. and Noiman, S. and Becker, O. M.},
title = {{PREDICT} modeling and in-silico screening for {G}-protein coupled
receptors.},
journal = {Proteins},
year = {2004},
volume = {57},
pages = {51--86},
number = {1},
month = {Oct},
abstract = {G-protein coupled receptors (GPCRs) are a major group of drug targets
for which only one x-ray structure is known (the nondrugable rhodopsin),
limiting the application of structure-based drug discovery to GPCRs.
In this paper we present the details of PREDICT, a new algorithmic
approach for modeling the 3D structure of GPCRs without relying on
homology to rhodopsin. PREDICT, which focuses on the transmembrane
domain of GPCRs, starts from the primary sequence of the receptor,
simultaneously optimizing multiple 'decoy' conformations of the protein
in order to find its most stable structure, culminating in a virtual
receptor-ligand complex. In this paper we present a comprehensive
analysis of three PREDICT models for the dopamine D2, neurokinin
NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the
CCR3 receptor model is also included. All models were found to be
in good agreement with a large body of experimental data. The quality
of the PREDICT models, at least for drug discovery purposes, was
evaluated by their successful utilization in in-silico screening.
Virtual screening using all three PREDICT models yielded enrichment
factors 9-fold to 44-fold better than random screening. Namely, the
PREDICT models can be used to identify active small-molecule ligands
embedded in large compound libraries with an efficiency comparable
to that obtained using crystal structures for non-GPCR targets.},
doi = {10.1002/prot.20195},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15326594},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1002/prot.20195}
}
@article{Shivakumar2009Structural,
author = {Pavithra Shivakumar and Michael Krauthammer},
title = {Structural similarity assessment for drug sensitivity prediction
in cancer.},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10 Suppl 9},
pages = {S17},
abstract = {BACKGROUND: The ability to predict drug sensitivity in cancer is one
of the exciting promises of pharmacogenomic research. Several groups
have demonstrated the ability to predict drug sensitivity by integrating
chemo-sensitivity data and associated gene expression measurements
from large anti-cancer drug screens such as NCI-60. The general approach
is based on comparing gene expression measurements from sensitive
and resistant cancer cell lines and deriving drug sensitivity profiles
consisting of lists of genes whose expression is predictive of response
to a drug. Importantly, it has been shown that such profiles are
generic and can be applied to cancer cell lines that are not part
of the anti-cancer screen. However, one limitation is that the profiles
can not be generated for untested drugs (i.e., drugs that are not
part of an anti-cancer drug screen). In this work, we propose using
an existing drug sensitivity profile for drug A as a substitute for
an untested drug B given high structural similarities between drugs
A and B. RESULTS: We first show that structural similarity between
pairs of compounds in the NCI-60 dataset highly correlates with the
similarity between their activities across the cancer cell lines.
This result shows that structurally similar drugs can be expected
to have a similar effect on cancer cell lines. We next set out to
test our hypothesis that we can use existing drug sensitivity profiles
as substitute profiles for untested drugs. In a cross-validation
experiment, we found that the use of substitute profiles is possible
without a significant loss of prediction accuracy if the substitute
profile was generated from a compound with high structural similarity
to the untested compound. CONCLUSION: Anti-cancer drug screens are
a valuable resource for generating omics-based drug sensitivity profiles.
We show that it is possible to extend the usefulness of existing
screens to untested drugs by deriving substitute sensitivity profiles
from structurally similar drugs part of the screen.},
doi = {10.1186/1471-2105-10-S9-S17},
pdf = {../local/Shivakumar2009Structural.pdf},
file = {Shivakumar2009Structural.pdf:Shivakumar2009Structural.pdf:PDF},
institution = {Department of Pathology, Yale University School of Medicine, New
Haven, CT, USA. pavithra.shivakumar@yale.edu},
keywords = {chemogenomics},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-10-S9-S17},
pmid = {19761571},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1186/1471-2105-10-S9-S17}
}
@article{Veber2002Molecular,
author = {Veber, D. F. and Johnson, S. R. and Cheng, H.-Y. and Smith, B. R.
and Ward, K. W. and Kopple, K. D.},
title = {Molecular properties that influence the oral bioavailability of drug
candidates},
journal = {J. Med. Chem.},
year = {2002},
volume = {45},
pages = {2615--2623},
number = {12},
month = {Jun},
abstract = {Oral bioavailability measurements in rats for over 1100 drug candidates
studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline)
have allowed us to analyze the relative importance of molecular properties
considered to influence that drug property. Reduced molecular flexibility,
as measured by the number of rotatable bonds, and low polar surface
area or total hydrogen bond count (sum of donors and acceptors) are
found to be important predictors of good oral bioavailability, independent
of molecular weight. That on average both the number of rotatable
bonds and polar surface area or hydrogen bond count tend to increase
with molecular weight may in part explain the success of the molecular
weight parameter in predicting oral bioavailability. The commonly
applied molecular weight cutoff at 500 does not itself significantly
separate compounds with poor oral bioavailability from those with
acceptable values in this extensive data set. Our observations suggest
that compounds which meet only the two criteria of (1) 10 or fewer
rotatable bonds and (2) polar surface area equal to or less than
140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a
high probability of good oral bioavailability in the rat. Data sets
for the artificial membrane permeation rate and for clearance in
the rat were also examined. Reduced polar surface area correlates
better with increased permeation rate than does lipophilicity (C
log P), and increased rotatable bond count has a negative effect
on the permeation rate. A threshold permeation rate is a prerequisite
of oral bioavailability. The rotatable bond count does not correlate
with the data examined here for the in vivo clearance rate in the
rat.},
keywords = {chemogenomics},
owner = {laurent},
pii = {jm020017n},
pmid = {12036371},
timestamp = {2008.07.16}
}
@article{Wassermann2009Ligand,
author = {Anne Mai Wassermann and Hanna Geppert and Jürgen Bajorath},
title = {Ligand prediction for orphan targets using support vector machines
and various target-ligand kernels is dominated by nearest neighbor
effects.},
journal = {J Chem Inf Model},
year = {2009},
volume = {49},
pages = {2155--2167},
number = {10},
month = {Oct},
abstract = {Support vector machine (SVM) calculations combining protein and small
molecule information have been applied to identify ligands for simulated
orphan targets (i.e., targets for which no ligands were available).
The combination of protein and ligand information was facilitated
through the design of target-ligand kernel functions that account
for pairwise ligand and target similarity. The design and biological
information content of such kernel functions was expected to play
a major role for target-directed ligand prediction. Therefore, a
variety of target-ligand kernels were implemented to capture different
types of target information including sequence, secondary structure,
tertiary structure, biophysical properties, ontologies, or structural
taxonomy. These kernels were tested in ligand predictions for simulated
orphan targets in two target protein systems characterized by the
presence of different intertarget relationships. Surprisingly, although
there were target- and set-specific differences in prediction rates
for alternative target-ligand kernels, the performance of these kernels
was overall similar and also similar to SVM linear combinations.
Test calculations designed to better understand possible reasons
for these observations revealed that ligand information provided
by nearest neighbors of orphan targets significantly influenced SVM
performance, much more so than the inclusion of protein information.
As long as ligands of closely related neighbors of orphan targets
were available for SVM learning, orphan target ligands could be well
predicted, regardless of the type and sophistication of the kernel
function that was used. These findings suggest simplified strategies
for SVM-based ligand prediction for orphan targets.},
doi = {10.1021/ci9002624},
pdf = {../local/Wassermann2009Ligand.pdf},
file = {Wassermann2009Ligand.pdf:Wassermann2009Ligand.pdf:PDF},
institution = {Department of Life Science Informatics, B-IT, LIMES Program Unit
Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität
Bonn, Dahlmannstrasse 2, D-53113 Bonn, Germany.},
keywords = {chemogenomics, chemoinformatics},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19780576},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1021/ci9002624}
}
@article{Yao2006Coupling,
author = {Yao, X. and Parnot, C. and Deupi, X. and Ratnala, V. R. P. and Swaminath,
G. and Farrens, D. and Kobilka, B.},
title = {Coupling ligand structure to specific conformational switches in
the beta2-adrenoceptor.},
journal = {Nat. Chem. Biol.},
year = {2006},
volume = {2},
pages = {417--422},
number = {8},
month = {Aug},
abstract = {G protein-coupled receptors (GPCRs) regulate a wide variety of physiological
functions in response to structurally diverse ligands ranging from
cations and small organic molecules to peptides and glycoproteins.
For many GPCRs, structurally related ligands can have diverse efficacy
profiles. To investigate the process of ligand binding and activation,
we used fluorescence spectroscopy to study the ability of ligands
having different efficacies to induce a specific conformational change
in the human beta2-adrenoceptor (beta2-AR). The 'ionic lock' is a
molecular switch found in rhodopsin-family GPCRs that has been proposed
to link the cytoplasmic ends of transmembrane domains 3 and 6 in
the inactive state. We found that most partial agonists were as effective
as full agonists in disrupting the ionic lock. Our results show that
disruption of this important molecular switch is necessary, but not
sufficient, for full activation of the beta2-AR.},
doi = {10.1038/nchembio801},
keywords = {chemogenomics},
owner = {laurent},
pii = {nchembio801},
pmid = {16799554},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1038/nchembio801}
}
@book{2006Chemical,
title = {Chemical Genomics: Small Molecule Probes to Study Cellular Function},
publisher = {Springer},
year = {2006},
editor = {Jaroch, S. E. and Weinmann, H.},
series = {Ernst Schering Research Foundation Workshop},
address = {Berlin},
keywords = {chemogenomics},
owner = {vert},
timestamp = {2007.08.02}
}
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Y. Acad. Sci.;Ann. Probab.;Ann. Stat.;Artif. Intell. Med.;Bernoulli;Bi
ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
r. Comput. Sci.;Trans. Am. Math. Soc.;Trends Genet.;}}
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