Chemical Information Bulletin, Volume 62, Number 1, Spring 2010 Page: 70
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Chemical Information Bulletin Vol. 62(1) Spring 2010
2D6, 2E1, as well as 3A4. Within this methodology,
topologically distinct regions of each ligand are
quantified using 540 2D and QM-based electronic
descriptors, and ranked according to their susceptibility
as metabolic sites. A quick and accurate tool for
making these predictions on substrate databases is now
available online.
CINF 138 Novel topological molecular key for
cheminformatics
P. Liu, pliu24@its.jnj.com, and D. Agrafiotis. Johnson
Johnson PRD, Exton, PA, United States
Representing molecules in the form of numerical or
symbolic keys is increasingly employed to capture the
structural characteristics and chemical properties in
chemoinformatics research. Here we present a novel
topological molecular key to encode the connecting
atoms and bonds into two separate components, which
allows the convenient comparison of each individual
component. We have applied this new key for the
screening stage of substructure search on a relational
database for a dataset of about 1 million compounds.
The screening efficiency with this new key is greater
than 99.9%. More importantly, this set of new keys can
uniquely determine the correct hits for certain generic
queries in a fraction of seconds, which is usually a
challenging task for other substructure search methods.
It also has potential in similarity search and clustering.
CINF 139 Classification of enzyme function based
on similarities in reaction mechanisms and common
substrate substructures
D. E. Almonacid, daniel.almonacid@ucsf.edu, and P.
C. Babbitt. Departments of Bioengineering and
Therapeutic Sciences, and Pharmaceutical Chemistry,
and California Institute for Quantitative Biosciences,
University of California San Francisco, San Francisco,
California, United States
Historically proteins have been studied and classified in
terms of their sequence and structure, and then often
independently associated with function. Sophisticated
mathematical methods have been developed to measure
similarities between protein sequences and structures,
generating robust classification schemes. In contrast,
for protein function, most, if not all, classification
systems are based on qualitative conceptual
frameworks rather than on quantitative measures. Here,
we classify enzyme molecular functions based on two
quantitative measures: (1) similarity of enzyme reaction
mechanisms and (2) common substrate substructures
between reactions. We apply this new classification to
families of evolutionarily related enzymes in ourStructure-Function Linkage Database. The results
indicate that similarities in reaction mechanisms and
substrate substructures are orthogonal and thus
complementary to classifications based on sequence
and structure. We discuss the value of quantification of
function similarity for functional prediction, annotation
and engineering of enzyme functions.
CINF 140 Chem BLAST: A rule-based method to
develop advanced structural ontologies for chemical
bioinformatics and the PDB, the PubChem
T. N. Bhat, bhat@nist.gov. CSTL, NIST, Gaithersburg,
MD, United States
Today's Chemical Bioinformatics community must
interact with a variety of information standalone
applications and ontologies. This limitation promotes
the need to define and develop rule-based stringent
ontologies for information processing and sharing.
Chemical Block Layered Alignment of Substructure
Technique (Chem-BLAST) first recursively dissects
chemical structures into blocks of substructures using
rules that operate on atomic connectivity and then
aligns them one against another to develop first
Chemical Resource Description Framework (RDF) and
then chemical ontologies in the form of a 'tree' made
up of 'hub-and-spoke'. The technique was applied for
(a) both 2-D and 3-D structural data for AIDS
(http://bioinfo.nist.gov/SemanticWeb_pr2d/chemblast.d
o ); (b) to I;60000 structures from the PDB which is
now available from the RCSB/PDB Web site
(http://www.rcsb.org/pdb/explore/extemalReferences.d
o?structureld=3GGT) and advanced features at
http://xpdb.nist.gov/chemblast/pdb.html . Full
description of the Chem_BLAST along with recent
results and illustrations including those for
approximately a million compounds from the PDB and
PubChem will be presented.
CINF 141 Chemical entity extraction and
interpretation
D. M. Lowe , d1387@cam.ac.uk, P. T. Corbett2, P.
Murray-Rust , and R. C. Glen'. 'Chemistry, University
of Cambridge, Cambridge, Cambridgeshire, United
Kingdom, 2Liguamatics, Cambridge, Cambridgeshire,
United Kingdom
OSCAR is an extensible Open-source system for
chemical entity recognition in text which has recently
been re-factored through UK eScience (OMII). Using
pattern based and machine learning techniques (with
interchangeable tools) OSCAR recognises chemical
compounds, reactions, enzyme names and other
chemical terms. To create structure searchable corpora70
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American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 62, Number 1, Spring 2010, periodical, Spring 2010; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc31514/m1/70/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .