Paper Info
Title
QSAR--how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets.
Abstract
The quality of QSAR (Quantitative Structure-Activity Relationships) predictions depends on a large number of factors including the descriptor set, the statistical method, and the data sets used. Here we study the quality of QSAR predictions mainly as a function of the data set and descriptor type using partial least squares as the statistical modeling method. The study makes use of the fact that we have access to a large number of data sets and to a variety of different QSAR descriptors. The main conclusions are that the quality of the predictions depends both on the data set and the descriptor used. The quality of the predictions correlates positively with the size of the data set and the range of biological activities. There is no clear dependence of the quality of the predictions on the complexity of the data set. All of the descriptors tested produced useful predictions for some of the data sets. None of the descriptors is best for all data sets; it is therefore necessary to test in each individual case, which descriptor produces the best model. In our tests, 2D fragment based descriptors usually performed better than simpler descriptors based on augmented atom types. Possible reasons for these observations are discussed.
Year
DOI
Venue
2006
10.1021/ci050413p
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Keywords
Field
DocType
statistical model,quantitative structure activity relationship,cross section,biological activity
Data mining,Quantitative structure–activity relationship,Data set,Partial least squares regression,Statistical model,Mathematics
Journal
Volume
Issue
ISSN
46
5
1549-9596
Citations 
PageRank 
References 
12
0.88
11
Authors
3
Name
Order
Citations
PageRank
Peter Gedeck1314.84
Bernhard Rohde2251.58
C. Bartels347336.82