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Stochastic Similarity Selections from Large Combinatorial Libraries Victor S. Lobanov* and Dimitris K. Agrafiotis
 

Summary: Stochastic Similarity Selections from Large Combinatorial Libraries
Victor S. Lobanov* and Dimitris K. Agrafiotis
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Suite 104, Exton, Pennsylvania 19341
Received August 31, 1999
A stochastic procedure for similarity searching in large virtual combinatorial libraries is presented. The
method avoids explicit enumeration and calculation of descriptors for every virtual compound, yet provides
an optimal or nearly optimal similarity selection in a reasonable time frame. It is based on the principle of
probability sampling and the recognition that each reagent is represented in a combinatorial library by multiple
products. The method proceeds in three stages. First, a small fraction of the products is selected at random
and ranked according to their similarity against the query structure. The top-ranking compounds are then
identified and deconvoluted into a list of "preferred" reagents. Finally, all the cross-products of these preferred
reagents are enumerated in an exhaustive manner, and systematically compared to the target to obtain the
final selection. This procedure has been applied to produce similarity selections from several virtual
combinatorial libraries, and the dependency of the quality of the selections on several selection parameters
has been analyzed.
INTRODUCTION
The explosive growth of combinatorial chemistry in recent
years has been greeted as both a blessing and a curse. While
it has solved the problem of throughput and has massively
parallelized the traditionally slow and ineffective drug

  

Source: Agrafiotis, Dimitris K. - Molecular Design and Informatics Group, Johnson & Johnson Pharmaceutical Research and Development

 

Collections: Chemistry; Computer Technologies and Information Sciences