Activity Cliffs Where QSAR Predictions Fail /
This brief introduces the readers of predictive cheminformatics to the concept of cliffs in the structure-activity landscape, which may greatly affect the data set modelability and the quality of predictions, hence generating disappointment from the performance of Quantitative Structure-Activity Rel...
Bewaard in:
| Hoofdauteurs: | , |
|---|---|
| Coauteur: | |
| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Editie: | 1st ed. 2025. |
| Reeks: | SpringerBriefs in Molecular Science,
|
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/978-3-032-10081-8 |
| Tags: |
Voeg label toe
Geen labels, Wees de eerste die dit record labelt!
|
Inhoudsopgave:
- QSAR – A tool of Predictive Cheminformatics
- Outliers and the Applicability Domain of QSAR models
- Cliffs in Biological Activity Landscape
- The Arithmetic Residuals in K-Group Analysis (ARKA) for the Detection of Activity Cliffs
- Activity Cliffs and Dataset Modelability – Future Roadmaps.