A Report on Fourth Joint Sheffield Conference on
Chemoinformatics June 18-20, University of Sheffield, UK
The initial part of Joelle Gola's talk
consisted of generalities about local and global models and the
features of BioFocusDPI's product Admensa Interactive.
Later on, Gola gave what approached a sales pitch about the proprietary
algorithm Glowing Molecule which highlights problematic regions within
potential drugs responsible for deficiencies in ADMET properties. Gola
intended to present case studies of his company's new automatic
techniques for model building. These enable non-computational
scientists to capture and share the knowledge contained in their
experimental data by building local models for individual chemical
series, iteratively improving models as more data are generated and
using new models to predict properties for new chemical structures.
Gola did describe the Gaussian Process method at the heart of this work10 but he ran out of time while rushing through his three examples.
Lastly, Damjan Krstajic, of the Research Centre for Cheminformatics in Serbia described yet another answer to the challenge of designing a system that will cope with constant influx of new information. Discovery Bus
aims to automate QSAR modeling without sacrificing the quality of
predictions. It is an implementation of Competitive Workflow, a novel
software architecture, implemented using autonomous software agents11.
All possible combinations of components are explored leading to
exhaustive evaluation of potential solutions. The idea is that we
cannot know in advance which technique or approach to use in solving a
QSAR problem, but if we apply most of the well known techniques and
approaches, then we will have an explosion in the number of models, but
we will also end up with multiple good solutions among them. A related
poster, by David Leahy and co-workers at Newcastle University,
described a multi-objective reverse QSAR search agent called Forager.
This has been developed to search for non-dominated solutions to the
research target profile definition of a new drug within a complex
descriptor space, where the search heuristics are provided by multiple
QSAR models. Forager uses a modified Particle Swarm Optimization
algorithm.
I have reported on only 10 out of 24 presentations. It is hoped that another report will appear in the CSA Trust newsletter. Abstracts
for all the papers and posters are online and readers are encouraged to
seek out the many interesting papers for which I have not had space to
comment. All in all, this was an excellent meeting. It is a shame that
it could not accommodate more delegates, but if it did so, perhaps some
of the more useful interactions and discussions would be impeded. I
shall make a point of booking early for the fifth incarnation of this
event.
References:
1. Schuffenhauer A.; Brown, N.; Ertl, P.;
Jenkins, J. L.; Selzer, P.; Hamon, J. Clustering and Rule-based
Classifications of Chemical Structures Evaluated in the Biological
Activity Space. J. Chem. Inf. Model. 2007, 47,325-336.
2. Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 1996, 39, 2887-2893.
3. Schuffenhauer, A.; Ertl, P.; Roggo, S.; Wetzel S.; Koch M. A.;
Waldmann, H. The Scaffold Tree - Visualization of the Scaffold Universe
by Hierarchical Scaffold Classification. J. Chem. Inf. Model. 2007, 47, 47-58.
4. Brewer, M. Development of a Spectral Clustering Method for the Analysis of Molecular Datasets. J. Chem. Inf. Model. 2007, in press.
5. Boda, K.; Seidel, T.; Gasteiger, J. Structure- and Reaction-based Evaluation of Synthetic Accessibility. J. Comput.-Aided Mol. Des. Published online February 9, 2007.
6. Terfloth, L.; Bienfait, B.; Gasteiger, J. Ligand-based Models for
the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9
Susbstrates. J. Chem. Inf. Model. 2007, in press.
7. Zhang, Q.-Y.; Aires-de-Sousa, J. Structure-based Classification of
Chemical Reactions without Assignment of Reaction Centers. J. Chem. Inf. Model. 2005, 45, 1775-1783.
8. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Similarity
Searching of Chemical Databases Using Atom Environment Descriptors
(MOLPRINT 2D): Evaluation of Performance. J. Chem. Inf. Comput. Sci. 2004, 44, 1708-1718.
9. Agrafiotis, D. K.; Xu, H. A Self-organizing Principle for Learning Nonlinear Manifolds. Proc. Natl. Acad. Sci. U. S. A. 2002, 99(25) 15869-15872.
10. Obrezanova, O.; Csányi, G.; Gola, J.; Segall, M. Gaussian
Processes: a Method for Automatic QSAR Modeling of ADME Properties. J. Chem. Inf. Model. 2007, in press.
11. Cartmell, J.; Enoch, S.; Krstajic, D.; Leahy, D. E. Automated QSPR through Competitive Workflow. J. Computer-aided Mol. Des. 2005, 19, 821-833.
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