10.1021/ci060132x.s002
Shuxing Zhang
Shuxing
Zhang
Alexander Golbraikh
Alexander
Golbraikh
Scott Oloff
Scott
Oloff
Harold Kohn
Harold
Kohn
Alexander Tropsha
Alexander
Tropsha
A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach:  Method
Development, Applications, and Virtual Screening of Chemical Databases Using
Validated ALL-QSAR Models
American Chemical Society
2006
toxicity IGC 50 values
48 anticonvulsant agents
test compound
training
chemical data sets
250 phenolic compounds
Novel Automated Lazy Learning QSAR
Tetrahymena pyriformis data
database screening
ED 50 values
2006-09-25 00:00:00
Journal contribution
https://acs.figshare.com/articles/journal_contribution/A_Novel_Automated_Lazy_Learning_QSAR_ALL_QSAR_Approach_Method_Development_Applications_and_Virtual_Screening_of_Chemical_Databases_Using_Validated_ALL_QSAR_Models/3057700
A 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 ED<sub>50</sub> values, 48 dopamine D<sub>1</sub>-receptor antagonists with known competitive
binding affinities (<i>K</i><i><sub>i</sub></i>), and a <i>Tetrahymena </i><i>pyriformis</i> data set containing 250 phenolic compounds with
toxicity IGC<sub>50</sub> 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.