Cargando…
Evaluating parameters for ligand-based modeling with random forest on sparse data sets
Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study...
| Autores principales: | , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2018
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755600/ https://www.ncbi.nlm.nih.gov/pubmed/30306349 http://dx.doi.org/10.1186/s13321-018-0304-9 |