Generation of QSAR sets with a self-organizing map.
A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon descriptors. The resultant classification is used to generate training, cross-validation (CV) and prediction sets for QSAR modeling using the ADAPT methodology. The results are compared to those of QSAR models generated using sets created by activity binning and a sphere exclusion method. The results indicate that the SOM is able to generate QSAR sets that are representative of the composition of the overall data set in terms of similarity. The resulting QSAR models are half the size of those published and have comparable RMS errors. Furthermore, the RMS errors of the QSAR sets are consistent, indicating good predictive capabilities as well as generalizability.
Department of Chemistry, Penn State University, 152 Davey Laboratory, University Park 16802, USA.
Rajarshi
R
Guha
Jon R
JR
Serra
Peter C
PC
Jurs
D000465:Algorithms; D005493:Folic Acid Antagonists / Q000138:chemical synthesis / Q000737:chemistry; D008962:Models, Theoretical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D011897:Random Allocation; D015203:Reproducibility of Results; D013762:Tetrahydrofolate Dehydrogenase / Q000378:metabolism
D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase