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Generation of QSAR sets with a self-organizing map.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Guha, Rajarashi Serra, Jon R. Jurs, Peter C. |
| Copyright Year | 2004 |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 14 |
| Page Count | 14 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.rguha.net/writing/pres/nyc_poster.pdf |
| PubMed reference number | 15331049v1 |
| Volume Number | 23 |
| Issue Number | 1 |
| Journal | Journal of molecular graphics & modelling |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Crisscross Heart Exclusion Quantitative Structure-Activity Relationship RMS Scientific Publication Self-Organizing Map dihydrofolate triangulation |
| Content Type | Text |
| Resource Type | Article |
