Abstract
QSAR already plays an important role in lead structure optimization and it can be predicted that QSAR methods will become essential for handling the huge amount of data associated with combinatorial chemistry. 3D QSAR has already been successfully applied to many data sets of enzyme and receptor ligands. The theory and methodology of these approaches were outlined in Part 1 of this article, published in the November issue. In the second part of this two-part review, the author explains the applications of these methods and addresses the associated problems.
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