Name
Title | ||
|---|---|---|
Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects. |
Abstract | ||
|---|---|---|
The most desirable compound leads from high-throughput assays are those with novel biological activities resulting from their action on a single biological target. Valuable resources can be wasted on compound leads with significant 'side effects' on additional biological targets; therefore, technical refinements to identify compounds that primarily have effects resulting from a single target are needed. This study explores the use of multiple assays of a chemical library and a statistic based on entropy to identify lead compound classes that have patterns of assay activity resulting primarily from small molecule action on a single target. This statistic, called the coincidence score, discriminates with 88% accuracy compound classes known to act primarily on a single target from compound classes with significant side effects on nonhomologous targets. Furthermore, a significant number of the compound classes predicted to have primarily single-target effects contain known bioactive compounds. We also show that a compound's known biological target or mechanism of action can often be suggested by its pattern of activities in multiple assays. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1021/ci050495h | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Keywords | Field | DocType |
side effect,high throughput screening | Lead compound,High-throughput screening,Combinatorial chemistry,Chemical library,Chemistry,Small molecule,Biological target,Mechanism of action | Journal |
Volume | Issue | ISSN |
46 | 4 | 1549-9596 |
Citations | PageRank | References |
1 | 0.39 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Klekota | 1 | 35 | 2.48 |
| Erik Brauner | 2 | 4 | 0.88 |
| Frederick P Roth | 3 | 345 | 34.18 |
| Stuart L Schreiber | 4 | 97 | 14.41 |