Chemical Information Bulletin, Volume 61, Number 1, Spring 2009 Page: 50 of 56
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and processes. Every pharma company & software
vendor is challenged by the technical interconversion,
collation and interpretation of drug/agrochemical
discovery data and as such, there is a vast amount of
duplication, conversion and testing that could be reduced
if a common foundation of data standards, ontologies
and web-services could be promoted and ideally agreed
within a nonproprietary and non-competitive framework.
This would allow interoperability between a traditionally
diverse set of technologies to benefit the healthcare
sector. Through global collaboration, this pragmatic
community will derive and instantiate and make
available web-services for consumption by Academic
institutions, Vendors and Companies under an Open
Source framework. We will describe current progress,
learnings and how companies, academics and others can
participate in this approach.
CINF 78. Cleaning up chemistry for the pharma
industry: Delivering a flexible platform for
interrogating the FDA DailyMed website. A.
Williams, antony. williams@chemspider.com, ChemZoo
Inc, 904 Tamaras Circle, Wake Forest, NC 27587, and
Rudy Potenzone, rudolphp@microsoft.com, World Wide
Industry Technology Strategies, MicrosoJt, WA
DailyMed is a website hosted by the FDA providing
access to information about marketed drugs. This
information includes FDA approved labels (package
inserts) and provides a standard, comprehensive, up-to-
date, look-up and download resource of medication
content and labeling as found in medication package
inserts. With an intention of enhancing the dataset by
making it searchable by chemical structure/substructure
we determined that the data contained numerous
chemistry errors. We have therefore used a combination
of text-mining, automated and manual curation to
improve the quality of the data set. In so doing we have
also made querying of the data more flexible.
Specifically we have used the Microsoft SharePoint
technology to create a portal allowing both text-based
and structure-based querying. We will report on the
advantages such an approach delivers in terms of flexible
interrogation of DailyMed.
CINF 79. The use of EPA software and Scranton
University green chemistry web page in the green
engineering course in Universidad de los Andes.
Gabriel Camargo, gcamargo@uniandes.edu.co,
Department of Chemical Engineering, Universidad de
Los Andes, Cra IE # 19 A - 40, Bogota, Colombia,
Francisco Segura, fr-segur@uniandes. edu. co, Chemical
Engineering Dpt, Universidad de los Andes, Bogota,
Colombia, Astrid Altamar, aaltamarposgrado
Cunilibre. edu. co, Environmental Engineering,
Postgraduated Institute, Cra 70 No 53 - 40, Bogota D.C, Colombia, and Joaquin E Tirano, jtirano
@uniandes.edu.co, Chemical Engineering Department,
Universidad de los Andes, Bogota, Colombia
The course of green engineering was proposed as an
elective signature in Universidad de los Andes. For de
development of this course a planned activities was
carried out. Three laboratories practice were done;
synthesis of catalysts supports, elaboration of biodiesel
and glycerin oxidation by heterogeneous catalysis with
impregnated catalysts. In these practices the greenness of
the processes were followed by measure of the material
balance and waste generated and the EPI suite program
was used to evaluate the environmental performance of
the reactants intermediates and products substances.
Other software available, in the EPA web page, was used
too. The green chemistry web page at Scranton
University was used to evaluate the engineering aspects
of the different green topics. The bibliographic resources
of Universidad de los Andes Library was used for more
information about the green topics. Other web page was
used for the student in the catalysts characterizations
techniques, with good results
CINF 80. Building blocks for automated elucidation
of metabolites: Machine learning methods for NMR
prediction. Christoph Steinbeck', steinbeck@ebi.ac. uk,
SteJan Kuhn', Steffen Neumann2, Bjorn Egert2, and
Gilleain Torrance'. (1) Chemoinbormatics and
Metabolism, European Bioinjbrmatics Institute (EBI),
Wellcome Trust Genome Campus, Cambridge CB10
ISD, United Kingdom, (2) Department of Stress and
Developmental Biology, Leibniz Institute of Plant
Biochemistry, Halle 06120, Germany
Current efforts in Metabolomics, such as the Human
Metabolome Project, collect structures of biological
metabolites as well as data for their characterisation,
such as spectra for identification of substances and
measurements of their concentration. Still, only a
fraction of existing metabolites and their spectral
fingerprints are known. Computer-Assisted Structure
Elucidation (CASE) of biological metabolites will be an
important tool to leverage this lack of knowledge.
Indispensable for CASE are modules to predict spectra
for hypothetical structures. This talk describes our
experiments with different statistical and machine
learning methods to perform predictions of proton NMR
spectra based on data from our open database
NMRShiftDB [1]. A mean absolute error of 0.18 ppm
was achieved for the prediction of proton NMR shifts
ranging from 0 to 11 ppm. Random forest, J48 decision
tree and support vector machines achieved similar
overall errors. NMR prediction methods applied in the
course of this work delivered precise predictions which
can serve as a building block for Computer-AssistedChemical Information Bulletin, Vol. 61, No 1 (Spring) 2009
48
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American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 61, Number 1, Spring 2009, periodical, Spring 2009; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc11508/m1/50/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .