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A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models
journal contribution
posted on 2006-09-25, 00:00 authored by Shuxing Zhang, Alexander Golbraikh, Scott Oloff, Harold Kohn, Alexander TropshaA novel automated lazy learning quantitative structure−activity relationship (ALL-QSAR) modeling approach
has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted
from a locally weighted linear regression model using chemical descriptors and the biological activity of
the training set compounds most chemically similar to this test compound. The weights with which training
set compounds are included in the regression depend on the similarity of those compounds to a test compound.
We have applied the ALL-QSAR method to several experimental chemical data sets including 48
anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive
binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with
toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents
identified several known anticonvulsant compounds that were not only absent in the training set but highly
chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be
further exploited as a general tool for accurate bioactivity prediction and database screening in drug design
and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited
for the development of highly predictive models for the sparse or unevenly distributed data sets.