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A. Kos, V. Poroikov, D. Toman, U. Jordis, T. Knuuttila:
"Finding new acetylcholinesterase inhibitors using data mining technologies";
Talk: Abstracts of Papers, 223rd ACS National Meeting, Orlando, FL USA; 04-07-2002 - 04-11-2002; in: "Abstracts of Papers", (2002), MEDI-015.

Data mining is finding correlations between data that are not obvious, and for which one cannot, or has not searched specifically. Data by itself are meaningless. Only the correlation of data creates knowledge. One way of data mining in chem. is the clustering of chem. structure databases. This clustering, or if less rigorous, "grouping" of structures, is done by numerical approaches, numerical indirect ways, and without any numerical processing by using visualization software. We will illustrate clustering in the biol. space using the PASS (Prediction of Activity Spectra of Substances) parameters, and will compare this to clustering in the chem. structure space using the MDL mol keys. PASS uses a large knowledge base of known active compds. to predict up to thousand biol. activities. Numerical clustering is done with a fast tree based implementation of self-organizing maps (non-supervised neural network) and for data visualization or "clustering by browsing" we use miner3d.excel. With the example of finding new active acetylcholinesterase inhibitors, we will demonstrate an application of data mining and in silico screening.