Teckentrup A, Briem H, Gasteiger J. “Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits.” J. Chem. Inf. Model.. 2004;44(2):626-634.
| Title | Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits |
| Publication Type | Journal Article |
| Year of Publication | 2004 |
| Authors | Teckentrup A, Briem H, Gasteiger J |
| Journal | J. Chem. Inf. Model. |
| Volume | 44 |
| Issue | 2 |
| Pagination | 626 – 634 |
| Date Published | 03/2004 |
| ISSN | 1549-9596 |
| Abstract | Kohonen neural networks generate projections of large data sets defined in high-dimensional space. The resulting self-organizing maps can be used in many applications in the drug discovery process, such as to analyze combinatorial libraries for their similarity or diversity and to select descriptors for structure−activity relationships. The ability to investigate thousands of compounds in parallel also allows one to conduct a study based on single-dose experiments of high-throughput screening campaigns, which are known to have a greater uncertainty than IC50 or Ki values. This is demonstrated here for a data set of 5513 compounds from one combinatorial library. Furthermore, a method was developed that uses self-organizing maps not only as an indicator of structure−activity relationships, but as the basis of a classification system allowing predictive modeling of combinatorial libraries. |
| DOI | 10.1021/ci034223v |
| Short Title | J. Chem. Inf. Model. |