Cargando…
Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accu...
Autores principales: | Ain, Qurrat Ul, Aleksandrova, Antoniya, Roessler, Florian D., Ballester, Pedro J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832270/ https://www.ncbi.nlm.nih.gov/pubmed/27110292 http://dx.doi.org/10.1002/wcms.1225 |
Ejemplares similares
-
Performance of machine-learning scoring functions in structure-based virtual screening
por: Wójcikowski, Maciej, et al.
Publicado: (2017) -
The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction
por: Li, Hongjian, et al.
Publicado: (2018) -
BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences
por: Dahlö, Martin, et al.
Publicado: (2015) -
Machine learning methods in chemoinformatics
por: Mitchell, John B O
Publicado: (2014) -
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
por: Li, Hongjian, et al.
Publicado: (2014)