Free online resources:Layout 1 14/1/10 19:53 Page 38
Cheminformatics
Continued from page 37 becoming increasingly available. In parallel we are cheminformatics models have been almost absent
seeing more discussion about the need for more in the published literature. It appears that chemin-
22 Zvinavashe, E et al (2008).
pre-competitive
32-35
, competitive
36
and collabora- formaticians and molecular modellers tend to take
Promises and pitfalls of
quantitative structure-activity
tive approaches
12,32
in drug discovery and the published chemical and biological data at their face
relationship approaches for
pharmaceutical industry in general, covering areas value and launch calculations without carefully
predicting metabolism and such as informatics, ADME/tox and clinical. This examining the accuracy of data records. However,
toxicity. Chem Res Toxicol 21
raises the question: “What could we achieve by just there should be much less disagreement concerning
(12), 2229-2236.
making more software and data resources avail- the exact chemical structure of compounds in the
23 Johnson, SR (2008). The
trouble with QSAR (or how I
able on the web?” There is currently little in the databases except for arguably difficult issues such
learned to stop worrying and
way of freely available resources for computation- as tautomers. Thus, the accuracy of the chemical
embrace fallacy). J Chem Inf al ADME/Tox (apart from efforts like the ToxCast structure representation could be addressed direct-
Model 48 (1), 25-26.
project
37,38
at the EPA where several hundred ly in most cases.
24 Tropsha, A and Golbraikh, A
compounds have been screened in more than 600 Both common sense and the recent QSAR investi-
(2007). Predictive QSAR
modeling workflow, model
biological assays and the results have been made gations described above indicate that chemical
applicability domains, and
public, representing a resource for future models) record curation should be viewed as a separate and
virtual screening. Curr Pharm so when will this change? Perhaps, as more data is perhaps critical component of cheminformatics
Des 13 (34), 3494-3504.
placed in the public domain by companies that are research. By comparison, the community of protein
25 Oprea, TI et al (2009). A
holding on to it closely. If more computational x-ray crystallographers has long recognised the
crowdsourcing evaluation of
the NIH chemical probes. Nat
tools and biological data were freely available it importance of structural data curation; indeed the
Chem Biol 5 (7), 441-447.
would facilitate crowd-sourced drug discovery and Protein Data Bank (PDB) team includes a large
26 Walters, WP and Murcko, basically level the playing field for small (or one- group of structure curators whose only job is to
MA (2002). Prediction of ‘drug-
person) virtual companies versus other pharma process and validate primary data submitted to the
likeness’. Adv Drug Del Rev
and biotech without requiring expensive tools and PDB by experimental crystallographers
39
.
54, 255-271.
27 Hann, M et al (1999).
databases (eg CAS SciFinder). In this case, anyone Furthermore, the NIH recently awarded a signifi-
Strategic pooling of
with access to a computer anywhere in the world cant Center grant to a group of scientists from the
compounds for high- can contribute to drug discovery regardless of University of Michigan (http://www.genome
throughput screening. J Chem
whether they belong to a company, research insti-
web.com/informatics/nigms-allots-5m-new-data-
Inf Comput Sci 39 (5), 897-902.
tute or not. Young gamers are already contributing base-house-protein-ligand-data-pharma-contribute)
28 Pearce, BC et al (2006). An
empirical process for the
to the optimised folding of proteins as evidenced to curate primary data on protein-ligand complexes
design of high-throughput
by the success in the Community-Wide Experiment deposited to the PDB. Conversely, the largest pub-
screening deck filters. J Chem on the Critical Assessment of Techniques for licly funded cheminformatics project, ie, PubChem,
Inf Model 46 (3), 1060-1068.
Protein Structure Prediction, or CASP. is considered a data repository and no special effort
29 Huth, JR et al (2005).
(http://www.wired.com/medtech/genetics/magazin is dedicated to the curation of structural informa-
ALARM NMR: a rapid and
robust experimental method
e/17-05/ff_protein). Such efforts represent truly tion deposited to PubChem by the various contribu-
to detect reactive false
distributed discovery and could contribute to fully tors. Chemical data curation has been addressed
positives in biochemical integrated pharmaceutical networks. When this whenever possible by the privately funded, but pub-
screens. J Am Chem Soc 127
occurs there will be more of a need to work with licly available, ChemSpider project as well as by sev-
(1), 217-224.
highly dispersed individual researchers, store their eral other projects reviewed above. It is critical that
30 Huth, JR et al (2007).
Toxicological evaluation of
data and possibly take molecules to the next step, scientists who exploit and build models of datasets
thiol-reactive compounds
eg enabling preclinical testing, animal studies etc. derived from current databases or extracted from
identified using a la assay to This will then require companies such as publications dedicate their own effort to the task of
detect reactive molecules by
AssayDepot (http://www.assaydepot.com/) and data curation.
nuclear magnetic resonance.
CDD to help generate and store data needed for Of course the hope of using cheminformatics
Chem Res Toxicol 20 (12),
1752-1759.
progressing molecules to clinical studies and find- and databases in drug discovery is to increase the
31 Metz, JT et al (2007).
ing larger companies or organisations to take these efficiency and quality of molecules that progress
Enhancement of chemical rules further. We are seeing a shift from requiring pow- to later stages. Just identifying reactive molecules
for predicting compound
erful computers within insular organisations to do and false positives could be of great utility to the
reactivity towards protein
drug discovery to using resources on the web, and many groups that are not aware of this problem
thiol groups. J Comput Aided
Mol Des 21 (1-3), 139-144.
so this opens up being able to use cheap portable and avoid dead ends. If we really are to empower
32 Louise-May, S et al (2009).
and mobile devices to search databases and gener- the user and do crowd-sourced drug discovery, we
Towards integrated web-based ate predictions from computational models. Of will create issues with IP and the ownership of the
tools in drug discovery. Touch
course the quality of the output will be highly collaborative discovery. This consideration could
Briefings – Drug Discovery in
dependent on the initial data quality. be one of the reasons why this approach has not
Press.
Surprisingly, the investigations into how primary been followed before. Additionally, if we are to
Continued on page 39 data quality influences the quality of published identify gaps in the free tools to crowd-sourced
38 Drug Discovery World Winter 2009/10
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