Chemical Information Bulletin, Volume 61, Number 1, Spring 2009 Page: 44 of 56
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CINF 54. 2-D and 3-D adaptive scoring functions for
iterative kinase medium-throughput screening
(ikMTS) with Profile-QSAR and AutoShim. Eric J.
Martin, eric.martin@novartis.com, Novartis Institute for
Biomedical Research, 4560 Horton St, Emeryville, CA
94530, Fax: 510-923-2010, David C. Sullivan, Anacor
Pharmaceuticals, Inc, Palo Alto, CA 94303, CA 94530,
and Prasenjit Mukherjee, pkmukher@olemiss. edu,
Novartis Institute fbr Biomedical Research, Emeryville,
CA 94608
Screening our 1.5 million compound archive requires 6
months and $1,000,000. Profile-QSAR is a novel kinase-
specific, fragment-based, 2D modeling method that
combines data for >100,000 compounds against >70
kinases to produce fast, accurate, kinase activity
predictions for iterative screening. Since fragment-based
methods loose accuracy for novel chemotypes, docking
is also employed. However, conventional docking
suffers 3 limitations: 1) it requires a target protein
structure, 2) is slow, and 3) does not correlate with
affinity. Using medium-throughput experimental activity
data, AutoShim adjusts pharmacophore ishimsi to
produce highly predictive, target-specific, scoring
functions. Over 5 months, our entire archive was pre-
docked into a iUniversal Kinase Surrogate Receptori of
16 diverse kinase crystal structures. AutoShim can now
be ishimmedi for new kinases with experimental binding
data to accurately predict activity for 1.5 million
compounds in hours instead of weeks, without a crystal
structure. Together, Profile-QSAR and AutoShim
produced effective iterative screens.
CINF 55. Combining quantitative data and
qualitative knowledge to score reaction energies.
Chloe-Agathe Azencottl, cazencot@ics. uci. edu,
Matthew A. Kayala', mkayalagics.uci.edu, and Pierre
Baldi2, pfbaldi@uci. edu. (1) Bren School of Information
and Computer Science, IGB at University of California,
Irvine, 6210 Donald Bren Hall, Irvine, CA 92697, (2)
Institutejbr Genomics and Bioinformatics, School of
Information and Computer Sciences, University of
California, Irvine, Irvine, CA 92697
Predictive scoring functions based on statistical learning
techniques generally require large amounts of
quantitative training data. Unfortunately this numerical
knowledge is usually unavailable or prohibitively
expensive to obtain. For practical application however,
experts often only require qualitatively precise results to
define accurate ranking orders. Inspired by the inherent
reaction prediction capability of human chemists, we
propose a novel machine learning technique in the
context of state energy calculations. QM/MM and wet
lab experiments can supply some quantitative energy
data, but are impractical to run on a large scale. Incontrast, chemists exhibit significant problem-solving
ability without making exact numerical calculations.
Rather, their decisions are based solely on qualitative
knowledge of trends and ranking orders in molecule
stability and reaction rates. Our method utilizes the
limited quantitative experimental data available together
with this qualitative information to yield score functions
accurate enough to reproduce the problem-solving
capability of human experts.
CINF 56. Drug development and solid form selection:
Multicomponent crystals. William Jones, wj O
@cam.ac. uk, Department of Chemistry, University of
Cambridge, Lensfield Road, CB2 IEW Cambridge, UK
Developing new and stable crystal forms for drug
product development remains a challenge from both a
commercial viewpoint as well as from our need to
further understand molecular aggregation and crystal
packing. Our understanding of molecular recognition,
supramolecular chemistry and crystallization phenomena
help in what is frequently referred to as icrystal
engineeringi. The ability to couple experimental
observations with data in the CSD presents real
opportunities. Multicomponent crystals (where two or
more distinct chemical species are present in the crystal)
is an area of particular interest to pharmaceutical
chemists where salts, hydrates and cocrystals (amongst
others) can all be possible outcomes of a crystallization
process. Screening for all possibilities becomes critical
and while addressing some of the above issues I will also
outline recent developments in mechanochemical
methods as a screening tool.
CINF 57. Supramolecular heterosynthons and their
role in cocrystal design. Mike Zaworotko,
xtal@usf:edu, Miranda L Cheney, mcheney@cas. usfedu,
and David Weyna, Department of Chemistry, University
of South Florida, CHE205, Tampa, FL 33647
Crystal engineering facilitates discovery of new crystal
forms for long known molecules that are of practical
utility such as active pharmaceutical ingredients, APIs.
This contribution will focus upon an emerging class of
crystal form, pharmaceutical cocrystals, with emphasis
upon the following:
- A historical perspective of this long known but little
studied class of compounds;
- Statistical analysis of the probability that certain
supramolecular heterosynthons will exist in the presence
of competing functional groups, i.e. how to select co-
crystal formers for APIs using statistics generated from
the Cambridge Structural Database;
- Examples of new co-crystals that include some long
known natural products and APIs and how they fine tune
physical properties of clinical relevance;Chemical Information Bulletin, Vol. 61, No 1 (Spring) 2009
42
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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/m1/44/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .