Chemical Information Bulletin, Volume 58, Number 2, Fall 2006 Page: 61
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CINF 120 In silico compound activity reprofiling. A.
W. Edith Chan', Richard J Fagan2, and John P
Overington2. (1) Inpharmatica, Commonwealth House,
1 New Oxford Street, WC1A 1NU London, United
Kingdom, Fax: +44 (0) 207 074 4700,
e.chan@inpharmatica.co.uk, (2) Inpharmatica Ltd
The human genome has offered potential novel drug
targets. This opportunity demands better ways to
translate potential molecular targets into diseaserelevant
therapeutics. Cell-based assays discover
compounds with activity against a signaling pathway
rather than a specific protein. Unfortunately,
identification of molecular target requires a labor
intensive, time consuming experimental strategy. The
Chematica platform enables molecular target prediction
by searching for chemical structural similarity in its
molecular databases. It consists of multiple
chemogenomics databases and various cheminformatics
and bioinformatics tools. These databases contain
highly curated compound, assay activity, molecular
target, and SAR data abstracted from 20 years of
medicinal chemistry literature. The premise is that if
two compounds are highly similar, their bioactivities
may be similar too. Target prediction was performed on
novel compounds identified in cell-based screen. 70%
of the compounds are highly similar to those in the
databases, and within which, about 25% were highly
confident target predictions.
CINF 121 Interplay of sequence and structure:
Extending the limits of detectability of distantlyrelated
proteins. Nathalie Meurice, F.N.R.S.
Postdoctoral Researcher, Department of Pharmacology
and Toxicology, University of Arizona, College of
Pharmacy, BioS Institute, Tucson, AZ 85721,
meurice @pharmacy.arizona.edu, Daniel P.
Vercauteren, Laboratoire de Physico-Chimie
Informatique, University of Namur, and Gerald M
Maggiora, Department of Pharmacology and
Toxicology, University of Arizona, College of
Pharmacy, BioS Institute, Tucson, AZ 85721,
maggiora @ pharmacy.arizona.edu
The combined efforts in genome sequencing projects
and structural genomics initiatives are generating
massive amounts of protein sequence and structure
data. However, molecular function remains unknown
for many of these proteins, even when their folds are
known. Thus, the relationship to homologs of known
function, if it exists, is likely very distant, and their
function cannot be reliably identified with sequencebased
methods alone. In this context, we carried out a
comprehensive analysis of sequence and structure
similarity of 18 proteins from the metzincin family that
indicates the increasing importance of structuralsimilarity when both sequence and structure have
diverged. Because structure diverges less than
sequence in remote homologs, structure-derived
patterns can reveal the features that traditional
sequence comparison methods cannot possibly
capture. In extreme cases, functional residues are
isolated in 3-D space and sequences differ to such an
extent that related proteins can only be detected from
3-D structure.
CINF 122 Interpretable correlation descriptors
for quantitative structure-activity relationships.
James L. Melville and Jonathan D. Hirst, School of
Chemistry, University of Nottingham, University
Park, Nottingham NG7 2RD, United Kingdom,
james.melville @nottingham.ac.uk
New, Topological Maximum Cross Correlation
(TMACC) descriptors for the derivation of
quantitative structure-activity relationships (QSARs)
are presented based on the widely used
autocorrelation method. They do not require the
calculation of three-dimensional structures, or
alignment. We have validated the TMACC
descriptors across eight literature datasets, ranging in
size from 66-361 molecules. In combination with
partial least squares regression, they perform
competitively with a current state-of-the-art 2D
QSAR methodology, hologram QSAR (HQSAR),
yielding superior leave-one-out cross validated
coefficient of determination values (LOO q2) for six
datasets, illustrating their wide applicability. Like
HQSAR, these descriptors are also interpretable, but
do not requiring hashing.
CINF 123 Local lazy regression: Making use of
the neighborhood to improve QSAR predictions.
Rajarshi Guha', Debojyoti Dutta2, Peter C. Jurs',
and Ting Chen2. (1) Department of Chemistry,
Pennsylvania State University, 104 Chemistry
Building, University Park, State College, PA 16802,
rxg218 @psu.edu, (2) Department of Computational
Biology, University of Southern California
Traditional QSAR models aim to capture global
structure-activity trends. In many situations there
may be groups of molecules which exhibit a specific
set of features which relate to their activity. Such a
group of features can be said to represent a local
structure-activity relationship. We describe the use of
local lazy regression which obtains a prediction for a
query molecule using its local neighborhood, rather
than considering the whole dataset. This modeling
approach is useful for large datasets since no a priori
model is built. We have applied the method to three
biological datasets where we observe improvementsChemical Information Bulletin
61
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American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 58, Number 2, Fall 2006, periodical, Autumn 2006; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc4981/m1/63/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .