This document discusses an in silico discovery approach to identify selective and high affinity inhibitors of the metabotropic glutamate receptor 3 (mGluR3). The researchers created a pharmacophore model of mGluR3 and used it to screen over 18 million drug-like compounds from the ZINC database. Compounds matching the model were docked into the receptor using Autodock Vina software. Over 3 million compounds were tested, with more than 130 having a predicted binding energy below -9.6 kcal/mol. The top 18 compounds below -10 kcal/mol were selected for further analysis in a bioassay to determine potency and selectivity for mGluR3. Preliminary results suggest this in silico

1. In Silico discovery of Metabotropic Glutamate Receptor-3 (mGluR-3) inhibitors
Juan E. Maldonado Weng1
, Walter I. Silva, PhD.2
and Héctor M. Maldonado, PhD.3
1
Universidad de Puerto Rico, Cayey, Puerto Rico; 2
University of Puerto Rico, Medical Science Campus; 3
Universidad
Central del Caribe, Medical School
Abstract
Glutamate is an excitatory neurotransmitter associated with many important brain functions. The metabotropic glutamate receptor3
(mGluR3) is an inhibitory auto-receptor that regulates glutamate presynaptic release through the use of G proteins. Although still a
controversial topic, a large number of scientific reports have suggested that this receptor is associated with many neurological disorders,
including a variety of psychiatric conditions. Discovery of highly selective mGluR3 (small chemical compounds) antagonists could lead
to more conclusive evidence since most of the currently available inhibitors target both mGluR2 and mGluR3. Moreover, drug-like
compounds with high affinity and selectivity for this receptor will have broad potential as psychopharmacological agents that can be
useful for treatment of several psychiatric conditions. In the other hand, advances in computer hardware and software have allowed
for the rapid development of computer-aid “In Silico” methodologies for the screening of large databases of small chemical compounds.
Therefore, as part of this research project we are testing the hypothesis that: “Selective and high affinity inhibitors of mGluR-3 can be
found using our Drug Discovery Strategy based on our novel “In Silico” approach”. We employed this innovative In Silico methodology
for the screening of a massive quantity of drug-like small chemical compounds for possible candidates with high affinity for the target
receptor. To that end, the 3D structure of the target receptor was analyzed for potential for chemical interactions or features. A
pharmacophore model was created (Ligand Scout software) based on those predicted features and used to filter (ZincPharmer
pharmacophore search software; zincpharmer.csb.pitt.edu/) a large (>18 million) drug-like compounds (ZINC drug-like database;
www.zinc.org), and only compounds fulfilling all requirements imposed by the model where selected for further analysis. Docking of
the selected group of compounds where performed in a high performance computer facility (UPR-HPCf; www.hpcf.upr.edu/) with the
aid of Autodock Vina software. Results from this part of the study where organized and compounds ranked according to their predicted
binding energy. Over three million compounds where tested with >130 compounds found to have a predicted binding energy below -
9.6 kcal/mol. From this group we have selected the top 18 compounds (binding energies below -10.0 kcal/mol) for further analysis in a
bioassay for potency and selectivity for mGluR3 receptor. Based on these preliminary results we can conclude that our In Silico approach
has resulted in the identification of several compounds as candidates for metabotropic glutamate receptor 3 inhibitors. Potency and
selectivity of these compounds remains to be determined in future studies employing an appropriate bioassay.
I. Background
Glutamate
Glutamate is an important neurotransmitter that plays major roles in the Central Nervous
System. This neurotransmitter is an ionized state of glutamic acid and very abundant in human
brains. A glutamate vesicle contains a concentration of up to 100mmol/L. This release of
glutamate results in an excitatory postsynaptic potential which leads to other signal pathways
(Meldrum 2000). Additionally, glutamate is classified as an excitatory neurotransmitter for its
capacity to cause powerful responses in neurons. Its excitatory abilities are utilized in many
pathways to create fast and responsive stimuli.
Glutamate has key roles in many functions that are necessary for development such as learning
and memory (Meyer, 2013). This neurotransmitter has part in neuronal differentiation and
survival in brain development. Furthermore, this important characteristic of glutamate is the
result of permitting the entry of calcium ions (Meldrum 2000). Glutamate has also been thought
to be involved in numerous neurodegenerative conditions such as schizophrenia, Alzheimer’s,
and amyotrophic lateral sclerosis (ALS) (Meyer 2013 & O'Brien 2014).

2. Maldonado 2
Receptors
Once released into the synapsis, glutamate can activate a broad range of receptors. The two
main classifications of glutamate receptors are Ionotropic and Metabotropic. Ionotropic-type
receptors are fast acting receptors with ion channels that are modulated by the presence of
specific neurotransmitters. Glutamate receptors are usually not highly selective and can interact
with other agonists with more selectivity. Those that would be placed into this category include:
AMPA, kainate and NMDA receptors. These receptors require the selective agonist for which
they are named after, as well as glutamate. These allow Na+ to enter the cell through their ion
channel; with exception of NMDA, which permits influx of both Na+ and Ca2+ (Meyer, 2013).
Glutamate can also interact with metabotropic-type receptors. Metabotropic receptors are a
slower type of receptors that are usually associated with prolonged stimulus. Moreover, these
receptors utilize second-messenger systems, where activation of the main protein causes a G
protein to carry on the message or function. These receptors share a common particular
morphology. Metabotropic glutamate receptors have similar seven trans-membrane domains,
N-terminal, and intracellular COOH terminal such as other G protein linked receptors (Meldrum,
2000). These receptors respond to glutamate and carry out their signal functions by manner of
the G-protein reactions. A total of eight (8) metabotropic glutamate receptors (mGluR) have been
described and further sub-divided into three groups (mGluR I, II, III) based on molecular identity
and type of signal transduction system activated. Activation of members of the metabotropic
glutamate receptors Group I can result in either increases in intracellular Ca++ concentration
(mGluR1) or activation of K+ ion channels (mGluR2). Members of Group II (mGluR2 and mGluR3)
are associated with inhibition of adenylyl cyclase that can result in reductions cAMP levels.
Finally, members of Group III metabotropic glutamate receptors (mGluR4, mGluR6, mGluR7,
mGluR8), are known to activate Ca++ channels, allowing influx of calcium inside the cells.
Furthermore, some of these receptors have an inhibitory effect on glutamate release, which in
turn manipulate the amount of glutamate in the synapse. These receptors have a wide array of
functions and roles in the synapse and within neurons and therefore can modulate a wide range
of physiological effects. As they are found widely distributed throughout the brain, many more
roles have been link to the metabotropic Glutamate Receptors.
Group II metabotropic glutamate receptors are located in both presynaptic and postsynaptic
neurons serving a variety of different functions. Presynaptic receptors belonging to this group
function as glutamate release inhibitors while as post-synaptic receptors, they serve as cAMP
formation inhibitors, which in turn could affect metabolism. Furthermore, they are also known
to activate MAPK (mitogen-activated protein kinase) and phosphatidylinositol-3-kinase
pathways, which will also lead to the synthesis of transforming growth factor-β (TGF- β).
Fortunately, this synthesis protects neurons from being overly excited from neurotransmitters
(retracted). In addition, they also regulate ion channels through the liberation of Gβγ subunits
(Conn 2010). Through their many functions, mGluR II are necessary components for many
cellular mechanisms.
The metabotropic glutamate receptor 3 (mGluR3) has been found to be associated with various
mental disorders. Chemical compounds with potential to exert pharmacological actions as

3. Maldonado 3
agonists, antagonists, or allosteric modulators of this receptor are currently been evaluated for
clinical applications. Examples include agonists like LY354740 with potential in the treatment of
anxiety and drug addiction (Monn 1997), and LY341495 an antagonist with antidepressant
properties (Pilc 2008). Group II mGlu-Receptors antagonists have been found to have positive
anti-depressant effects with a yet to be fully understood mechanism. Also, a study is looking to
affect the receptor through allosteric modulation (Campo 2011). Beneficially, a more selective
compound provides a more precise understanding of each receptor.
In Silico Discovery Approach
Utilizing our technological system, the receptor will be analyzed and compared against the many
compounds in the “Zinc Pharmer” database (Koes 2012). A pharmacophore model will be
created and will represent all the chemical features of the receptor. A pharmacophore model can
utilize two different methods to recreate the receptor: ligand-based or structure-based method.
The ligand-based method utilizes a set of known ligands. Alternately, structure-based method
use protein-ligand complex from readily available files to construct the model (Vourinen 2015).
A pharmacophore modeling software will construct the receptor model based on chemical traits
and prepare it for virtual screening process. Virtual screening process will provide lists of
numerous compounds readily available for further analysis. This would provide the candidates
for the receptor. The purpose of screening in 3D databases is to find compounds or hits with
similar chemical traits (Yang 2010).
The protein-ligand interaction will be heavily studied as the database will contain several
thousands of candidates. This would require the assistance of a high performance computer to
process the heavy amount of data. With the aid of high performance computing (Scholz 2012),
drug discovery has evolved with the addition of sophisticated drug design and high throughput
statistical algorithms ranking by order of potential potency numerous candidates for testing.
II. Methodology
As stated previously, this investigation will be carried out mostly In Silico. Fundamentally,
all results and procedures will hinge on computer processing power. This research design will
focus on compounds that interact with the virtually targeted receptor.
Firstly, since the method of modeling will be based on the protein-ligand complex designs,
the complex must be first obtained. The Protein Database (www.rcsb.org) will be the source for
obtaining the model. The mGluR3 receptor model that will be utilized was crystalized by
Wernimont and team (To be published). The mGluR3 is available with the compound LY341495
(Monn 1996). This compound is a known mGluR2/3 agonist. The model containing both
structures will be in a PDB format. PyMol (www.pymol.org) is the software where the model will
be first viewed. This program has the capacity to interpret all amino acids composing the
structure and the unique antagonist. This software will also separate the complex and save them
each as individual files.
Having separated the compound, the receptor is now available to be analyzed by
AutoDock (Morris 2009). This advanced software is capable computing a grid parameter file that

4. Maldonado 4
would predict ligand interactions. The UPR-High Performance Computer Facility (HPCf) systems
will execute a benzene mapping analysis utilizing the receptor file and the grid parameter
configuration file. AutoDock Vina (Trott 2009) software will carry out this function within the high
performance computer. AutoDock Vina is a successful docking program accredited for its high
accuracy and better scoring system (Trott 2010).
The result of this step would be a file containing a hundred or more different benzene
locations. These results are stored within one special file and will be retrieved from the HPCf.
These different configurations could easily be seen through PyMol. The model will be littered
with benzenes within the parameters of the grid made previously. Some benzenes will be in
clusters sharing similar locations within the receptor. The most efficient way to analyze these
results is to separate them into individual files and to choose the best benzene files that represent
those location-sharing benzene clusters. These selected benzenes will be utilized to form the
pharmacophore model.
A new file should contain the receptor and representative benzenes. This file will be used
in Ligand Scout (Wolber 2005). This software is highly utilized for creating both structure and
ligand based models. Ligand Scout will generate the pharmacophore model utilizing the receptor
and benzenes selected. A pharmacophore model is an abstract chemical representation of a
receptor (Wermuth 1998). Commonly, a pharmacophore model is constructed based on two
approaches. One approach would be to utilize training molecules, which are based on already
known ligand and interaction patterns, to guide the construction of the structure. Another, more
efficient, approach would see the model being built based on the ligand’s interaction with
another compound (Vuorinen, 2015). In this case, the model obtained from PDB will contain the
agonist structure of LY341495 interacting with the receptor. This structure-based approach
would provide accurate representation of the target as the software also analyzes the benzenes’
surrounding chemical interactions. The resulting combined model will contain all benzene
information. Visually, the pharmacophore model will be a collection of exclusion spheres that
represent the hydrophobic benzenes. The model will also contain arrows and other figures that
represent chemical bonds that could manifest chemical interactions. These spheres will be
located around a 3D space to represent the target receptor.
Next, this pharmacophore model will be uploaded unto ZINCPharmer (Koes 2012), a
virtual 3D database for compounds. This web-based search software is utilized for virtual
screening of commercially-available compounds in the ZINC database (Irwin 2005). This database
has over 35 million purchasable compounds in 3D available formats. The formats permit an
effective compound docking after the screening. ZINCPharmer interprets the parameters set by
the pharmacophore models and searches through the database. This screening software also
permits filtering compounds by molecular weight and number of rotatable bonds. After
screening, the result will be a spatial data file (SDF) containing the numerous amount of
compounds that will fit the special parameters set by the user.
This spatial data file will be translated into a Mol2 file, which is a more legible format. The
results within the newly-translated Mol2 file will be uploaded to the UPR-HPCf once more. Once
uploaded to the server within the facility, another special software will be utilized, Raccoon

5. Maldonado 5
(http://autodock.scripps.edu/resources/raccoon). As stated in the Scripps Research Institute,
“Raccoon is a graphical interface for preparing AutoDock virtual screenings”. This software is
useful for generating special computer scripts. Raccoon will convert all the results within the
Mol2 file into PDBQT files. This new file type is commonly used for AutoDock Vina’s virtual
screening hence the usefulness of Raccoon.
AutoDock Vina will run a virtual screen by docking all compounds obtained in the
ZINCPharmer screening. AutoDock Vina takes full advantage of the servers’ capabilities to run
the numerous compounds through rigorous ligand interaction analyzation of each individual
compound. The time the results will be available depends on the system capabilities and number
of compounds. With a special script, the results will be available in an easily readable table. This
permits the ranking of compounds by affinity, or binding energy. This investigation will focus on
the top five compounds in the list by affinity. These top five candidates will be extracted and
virtually compared with the 3D metabotropic Glutamate Receptor 3 structure in PyMol; this
would provide visual confirmation that this elaborate process has worked so far. The expected
model would be the newly-found compound positioned to interact with the benzenes found
within the receptor.
Further steps would include returning to the first ZINCPharmer screening and altering the
search and filter parameters. This further step would give a broader scope of the list of
compounds resulting from this screening. From previous In Silico investigations utilizing this
approach, altering search specifications will provide new compounds with higher affinity.
Another aspect to consider will be the benzenes utilized with the pharmacophore model. The
ZINCPharmer software also allows for slight modifications of the model. So another round of
tests would be to alter benzenes utilized for the search parameters. These results would all be
collected and analyzed.
III. Results
Fig1. The model obtained from the Protein Data Base. Presented are two different perspective of the receptor. The
model represents the metabotropic Glutamate receptor. The structure in green would be representative of ligand-
binding. The green structure is LY341495 antagonist and was included with the 3D model from PDB. Based on this
representation we conducted our experimentation.

6. Maldonado 6
Figure 2. Pharmacophore model representation of the target site within mGluR3 obtained from Ligandscout
software. This model is representative of the information obtained from ligand-receptor interactions from the
inhibitor LY341495 antagonist that was included in the protein database model. This model also is based on
information acquired from a benzene mapping procedure.
Analyzing the action site from the protein resulted in one hundred models each with its
own benzene. So we found four benzenes that would each be representative of those areas filled
with other benzenes, or clusters. From there, the best course of action was to find the most
favorable benzenes. This would require to understand which would yield the compounds with
most binding energy.
We performed the zinc database screening with three models. Each model derived from
the same pharmacophore model. Model A consisted in the use of the three closest benzenes
while removing the furthest (“Ben 4” from figure 2). The parameters for Model A’s screen was a
range in Molecular Weight from 350 to 450u with a range in rotatable bonds from 0 to 5 bonds.
Model B utilized all four benzenes. This model was screened with a range from 0 to 350 in
molecular weight and 0 to 5 rotatable bonds. Model C used three benzenes, removing the
benzene that was closest to the stacking benzenes (“Ben 2”). The parameters for the Zincpharmer
screen Model C were the same as with Model B.
The many compounds that have potential affinity towards the different pharmacophore
models were obtained through Zincpharmer (Wermuth, Ganellin and Lindberg) (Conn and Pin).
The number of compound are presented in table 1. This table presents the total amount of
compounds utilizing the range of molecular weight from 0 to 450u. This is to present the massive
scale of this project in terms of high input data.
Model Amount of Compounds
A 2,989,147 hits
B 197,655 hits
C 988,798 hits
Table 1. The result of screening in the Zincpharmer database. The models utilized have various alterations but are all
derivations of the mGluR3 receptor. The following parameters were utilized: [0≤Molecular Weight≤450; 0≤Rotatable
Bonds≤5] with no repeating structure.

7. Maldonado 7
The second screen analyses the binding energy between receptor and ligand. This analysis
is performed mostly by the HPCf. The binding energies between compound and target are
presented in table 2.
Model
Compounds with
Leading BE
A B C
-10.4 3 0 0
-10.3 0 0 0
-10.2 2 0 0
-10.1 1 1 1
-10 8 0 0
-9.9 11 3 4
-9.8 18 2 1
-9.7 17 4 9
-9.6 40 1 7
Table 2. The amount of compounds per binding energy for every model screened.
IV. Conclusion
The results indicate the possibility of many viable compounds to study. The most
outstanding results obtained were the compounds with a binding affinity of -10.4, all of which
were from model A’s screening. As seen in table 3, the most prominent model would be A as it
has the most compounds with the highest BE. Model A had all benzenes with the removal of “Ben
4” which increased the amount of compounds compared to model B’s use of all benzenes (table
1). As well as increasing the total amount of compounds, model A also had the most compounds
with the highest BE. Further analysis would be better suited for model A as it would yield most
promising results.
Since further study is required, the potential of compounds with leading binding energy
is yet to be determined. From the group of compounds with -10.0 BE and higher, there are a total
of eighteen (18) candidates with good potential, within the confines of this study.
V. Discussion
This study has reached its goal to find suitable compounds with high affinity towards the
designated receptor through the in silico methodology stated. Further steps will have to be
accomplish to determine the relation between compound and receptor. Follow up studies would
include understanding how to better increase binding energy by ligand modifications. Adding
specific properties would enhance the relation. We are currently seeing the extent of molecular
weight a molecule would react towards receptor. Understanding that a compound would have
to pass through the blood brain barrier, properties such as weight, size and lipid solubility should
be considered at all times (Banks, 2009).

8. Maldonado 8
This study was the first step in finding a pharmacological candidate to treat major
neurological disorders. The metabotropic glutamate receptor is potentially very important target
for the pharmacological treatment of addiction, depression, motor neurodegenerative diseases,
and schizophrenia (Campo, 2011) (Meldrum, 2000) (Moreno, 2009). As a necessary component
in synaptic glutamate release, many conditions are caused or associated with mGluR group II
dysfunction. Many antagonist target both receptors that pertain to this group (Cleva, 2012). With
this study, an antagonist may be formed that exclusively target the mGluR3. This would progress
many current studies that cannot source that actual cause of many neurological conditions.
VI. References
Banks, William A. "Characteristics of compounds that cross the blood-brain barrier." BMC
Neurology (2009).
Campo B, Kalinichev M, Lamberg N, El Yacoubi M, Royer-Urios I, Schneider M, Legrand C, Parron
D, Girard F, Bessif A, Poli S, Vaugeois J, Le Poul E, Celanire S. 2011. Characterization of an
mGluR2/3 Negative Allosteric Modulator in Rodent Models of Depression. Journal of
Neurogenetics [Internet]. [Cited 2015 Aug 25]; 25(4): 152–166. DOI:
10.3109/01677063.2011.627485
Cleva, Richard M. and M. Foster Olive. "Metabotropic glutamate receptors and drug addiction."
WIREs Membrane Transport and Signaling (2012): 281-295.
Conn, Jeffrey P. and Jean-Philippe Pin. "PHARMACOLOGY AND FUNCTIONS OF METABOTROPIC
GLUTAMATE RECEPTORS." Annual Review Pharmacology Toxicology (1997): 205-237.
Downing A, Kinon B, Millen B, Zhang L, Liu L, Morozova M, Brenner R, Rayle T, Nisenbaum L, Zhao
F, Gomez J. 2014. A double-blind, placebo-controlled comparator study of LY2140023
monohydrate in patients with schizophrenia. BMC Psychiatry [Internet]. [cited 2015 Aug 25];
14 (351): 1471-244. DOI: 10.1186/s12888-014-0351-3
Ekins, S, J Mestres and B Testa. "In silico pharmacology for drug discovery: methods for virtual
ligand screening and profiling." British Journal of Pharmacology (2007): 9-20.
Forli S. 2010. Raccoon|AutoDock VS: an automated tool for preparing AutoDock virtual
screenings. Available from: http://autodock.scripps.edu/resources/raccoon
Irwin J J, Shoichet B K. 2006. ZINC – A Free Database of Commercially Available Compounds for
Virtual Screening. Journal of Chemical Information and Modeling. [Internet]. [Cited 2015 Oct
15]. 45(1): 177-182 DOI: 10.1021/ci049714
Koes, David Ryan and Carlos J Camacho. "ZINCPharmer: pharmacophore search of the ZINC
database." Nucleic Acids Research (2012): W409–W414. Web Server issue.

9. Maldonado 9
Matosin N, Hons BMSc, Fernandez-Enright F, Frank E, Deng C, Wong J, Huang X, Newell K. 2014.
Metabotropic glutamate receptor mGluR2/3 and mGluR5 binding in anterior cingulate
cortex in psychotic and nonpsychotic depression, bipolar disorder and schizophrenia:
implications for novel mGluR-based therapeutics. Journal of Psychiatry of Neuroscience
[Internet]. [cited 2015 Aug 25]; 39(6): 407-416. DOI: 10.1503/jpn.130242
Meldrum, Brian S. "Glutamate as a Neurotransmitter in the Brain: Review of Physiology and
Pathology." American Society for Nutritional Sciences (2000): 1007S-1015S.
Monn J, Valli M J, Massey S M, Wright R A, Salhoff C R, Johnson B G, Howe T, Alt C A, Rhodes G
A, Robey R L, Griffey K R, Tizzano J P, Kallman M J, Helton D R, Schoepp D D. 1997. Design,
Synthesis, and Pharmacological Characterization of (+)-2-Aminobicyclo[3.1.0]hexane-2,6-
dicarboxylic Acid (LY354740): A Potent, Selective, and Orally Active Group 2 Metabotropic
Glutamate Receptor Agonist Possessing Anticonvulsant and Anxiolytic Properties. Journal
of Medicinal Chemistry. [Internet]. [Cited 2015 Oct 15]. 40: 528-537 DOI:
10.1021/jm9606756
Moreno, José L., Stuart C. Sealfon and Javier González-Maeso. "Group II metabotropic glutamate
receptors and schizophrenia." Cell Molecular Life Science (2009): 3777–3785.
Morris G M, Huey R, Lindstrom W, Sanner M F, Belew R K, Goodsell D S, Olson A J 2009 Autodock4
and AutoDockTools4: automated docking with selective receptor flexibility. Journal of
Computational Chemistry [Internet]. [Cited 2015 Oct 15]. 16: 2785-91. DOI:
10.1002/jcc.21256
Niwenger, Colleen M. and P. Jeffrey Conn. "Metabotropic Glutamate Receptors: Physiology,
Pharmacology, and Disease." Anual Review Pharmacology Toxicology (2010): 295–322.
O'Brien NL, Way MJ, Kandaswamy R, Fiorentino A, Sharp SI, Quadri G, Alex J, Anjorin A, Ball D,
Cherian R, Dar K, Gormez A, Guerrini I, Heydtmann M, Hillman A, Lankappa S, Lydall G, O'Kane
A, Patel S, Quested D, Smith I, Thomson AD, Bass NJ, Morgan MY, Curtis D, McQuillin A.). The
functional GRM3 Kozak sequence variant rs148754219 affects the risk of schizophrenia and
alcohol dependence as well as bipolar disorder. Psychiatr Genet. 2014 Dec;24(6):277-8.
Profaci C, Krolikowski K, Olszewski R, Neale J. 2011. Group II mGluR agonist LY354740 and NAAG
peptidase inhibitor effects on prepulse inhibition in PCP and D-amphetamine models of
schizophrenia. Psychopharmacology [Internet]. [Cited 2015 Aug 25]; 216(2): 235–243. DOI:
10.1007/s00213-011-2200-0
Trott O, Olson A J. 2010 AutoDock Vina: improving the speed and accuracy of docking with a new
scoring function, efficient optimization and multithreading. Journal of Computational
Chemistry [Internet]. [Cited 2015 Oct 15]. 31: 455-461. DOI: 10.1002/jcc.21334

10. Maldonado 10
Vuorinen A, Schuster D. 2015. Methods for generating and applying pharmacophore models as
virtual screening filters and for bioactivity profiling. Methods. [Internet]. [Cited 2015 Oct
15]. 71 (2015): 113-134 DOI: 10.1016/j.ymeth.2014.10.013
Walker A, Wenthur C, Xiang Z, Rook J, Emmitte K, Niswender C, Lindsley C, Conn P. 2015.
Metabotropic glutamate receptor 3 activation is required for long-term depression in
medial prefrontal cortex and fear extinction. Proceedings of National Academy of Sciences
of the United States of America [Internet]. [Cited 2015 Aug 25]; 112(4):1196-1201. DOI:
10.1073/pnas.1416196112
Wermuth, C. G., et al. "Glossary of Terms used in Medicinal Chemistry." Pure and Application
Chemistry (1998): 1129-1143.
Wernimont AK, Dong A, Seitova A, Crombet L, Khutoreskaya G, Edwards AM, Arrowsmith CH,
Bountra C, Weigelt J, Cossar D, Dobrovetsky E Wernimont AK, Dong A, Seitova A, Crombet
L, Khutoreskaya G, Edwards AM, Arrowsmith CH, Bountra C, Weigelt J, Cossar D,
Dobrovetsky E Crystal Structure of Metabotropic glutamate receptor 3 precursor in
presence of LY341495 antagonist. To be published.
Yang X, Wang G, Wang Y, Yue X. 2015. Association of metabotropic glutamate receptor 3
polymorphisms with schizophrenia risk: evidence from a meta-analysis. Neuropsychiatric
Disease and Treatment [Internet]. [Cited 2015 Aug 25]; 2015(11):823–833. Doi:
10.2147/NDT.S77966