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           COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER
                              College of Science
                       (CDS Department CSI 898-Sec 001)
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	 Cheminformatics: Linking chemical structure and biological 
          activity using machine learning and datamining techniques

                                 Alex Tropsha

               Chair, Division of Medicinal Chemistry and Natural Products, 
               School of Pharmacy, University of North Carolina
                             Chapel Hill, NC

                         
Cheminformatics was born due to rapid accumulation of chemical compound
related databases (containing the information on chemical structures and
their measured or sometimes computed properties such as bioactivities) that
should be organized, accessed, and explored. Thanks to the NIH's Roadmap
Program a lot of such data has become publicly available via the Molecular
Libraries Initiative (MLI) and the PubChem repository of biological assays
of chemical compounds. I shall describe the application of multiple machine
learning and datamining approaches (e.g., k Nearest Neighbor pattern
recognition, Support Vector Machines, graph mining) to the development of
Quantitative Structure-Activity Relationships (QSAR) models. Our approaches
afford robust and validated models capable of accurate prediction of
compound properties for molecules not included in the training sets. Such
models are used to screen virtual databases of existing or synthetically
feasible compounds and annotate these compounds with highly reliable
predicted properties. The focus on knowledge discovery and property
forecasting bring cheminformatics forward as predictive, as opposed to
evaluative scientific discipline.

                        Monday , October 1, 2007 
                                  4:30 pm
                     Room 301, Research I, Fairfax Campus

Refreshments will be served at 4:15 PM.
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Find the schedule at www.cmasc.gmu.edu/seminar/schedule.html
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