Chemical Information Bulletin, Volume 60, Number 2, Fall 2008 Page: 49 of 56
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CIN - 72. Topological polar surface area: A useful
desc iptor in 2-D-QSAR. Robert J. Doerksen and
Pras inna Sivaprakasam, Department of Medicinal
Che, iistry, School of Pharmacy, University of
Miss 'ssippi, 421 Faser Hall, University, MS 38677-
1848. Fax: 662-915-5638
Top( logical polar surface area (TPSA) is shown to be
a use ful descriptor in 2D-QSAR. TPSA is a convenient
measure of the polar surface area that avoids the need
to calculate ligand 3D structure or to decide which is
the relevant biological conformation or conformations.
This is the first report to demonstrate the value of
TPS,\ as a relevant descriptor applicable to a large,
struc urally and pharmacologically diverse set of
classes of compounds. We observed a negative
correlation of TPSA with activity data for anticancer
alkal aids, MT1 and MT2 agonists, MAO-B and tumor
necrosis factor-a inhibitors and a positive correlation
with inhibitory activity data for telomerase, PDE-5,
GSK-3, DNA-PK, aromatase, malaria,
trypz nosomatids and CB2 agonists.
CIN : 73. Automatic generation of predictive
property models. George D Purvis III',
GPu 'vis@us.fujitsu.con, David T Stanton, and
Willi am D Laidig~. (1) Biosciences Group, Fujitsu
Corn )uter Systems, 15244 NW Greenbrier Pkwy,
Beat erton, OR 97007, (2) Modeling and Simulations
Grol p, Procter & Gamble, Cincinnati, OH 45253
In the design of new products, specialized properties
that are unique to the use of that product are
optirlized. Products can be improved faster with
greater certainty that the improvements are the best
possible when predictive models assist the exploration.
Prop ietary ownership of product performance data
prevents development of useful models for these
properties outside of the companies that own the
prod icts and measure the data. We are developing an
auto nated tool for rapid creation of predictive
properties based on quantitative structure-property
relationships (QSPR) which enables owners of
prop 'ietary data to build their own predictive models.
In th s talk, we describe our optimization methodology
for finding QSPR relationships, the descriptors
incoi porated, the description of chemical space and the
methodology for error estimation based on QSPR
ense:nbles in the context of developing a predictive
QSP : model for boiling points.CINF 74. A status report on the InChI &
InChIKey project. Stephen R. Heller, Physical and
Chemical Properties Division, NIST, Gaithersburg,
MD 20899-8380
IUPAC has developed an algorithm to create a unique
chemical identifier- the InChI and a related fixed
length InChIKey. As opposed to other chemical
structure representations, with the InChI/InChIKey,
anyone, anywhere is able to create their own structure
files and databases using this public domain open
source algorithm. This means one is no longer
dependent on any outside source or organization for a
unique chemical identifier. Using InChI means you
can freely create and exchange structure files with
others within your organization and with any person or
organization anywhere in the world knowing the
structure name, the InChI/InChIKey, will be the same.
You can search for the InChI/InChIKey on the
Internet, using Google/Yahoo/Microsoft Live, and so
on. Using an InChI/InChIKey knowing you find a
match if it is there and not need to worry if it was
coded differently by another person or program.
InChI/InChIKey means you are no longer dependent
on any proprietary system and you are much more
likely be link to and to be linked from many, many
more chemists and sources of chemical information
than has been possible in the past. The
InChI/InChIKey is a system for both public and
private (internal proprietary and commercial fee-
based) sources. Details of the InChI/InChIKey and its
rapid and worldwide adoption will be presented.
CINF 75. Dynamic data evaluation for
thermodynamic properties of binary mixtures.
Chris D. Muzny, Vladimir V. Diky, Andrei Kazakov,
Eric W. Lemmon, Robert D. Chirico, and Michael
Frenkel, Physical and Chemical Properties Division,
National Institute of Standards and Technology, 325
Broadway, Boulder, CO 80305-3328, Fax: 303-497-
5044
Thermodynamic property data evaluation and the
subsequent production of data correlations or
equations of state for chemical compounds have
traditionally been performed by thermodynamicists
who are experts in this field. The concept of using an
expert software package to dynamically perform this
function was demonstrated for pure compounds with
the release of ThermoData Engine (TDE) 1.0, aChemical Information Bulletin, Vol. 60, No 2 (Fall) 2008
http://www.acscinf.org47
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American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 60, Number 2, Fall 2008, periodical, Autumn 2008; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc11506/m1/49/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .