Cargando…
Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins
BACKGROUND: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of...
Autores principales: | Ashtawy, Hossam M, Mahapatra, Nihar R |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416170/ https://www.ncbi.nlm.nih.gov/pubmed/25916860 http://dx.doi.org/10.1186/1471-2105-16-S6-S3 |
Ejemplares similares
-
BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
por: Ashtawy, Hossam M, et al.
Publicado: (2015) -
The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
por: Shen, Chao, et al.
Publicado: (2021) -
Superior Performance
of the SQM/COSMO Scoring Functions
in Native Pose Recognition of Diverse Protein–Ligand Complexes
in Cognate Docking
por: Ajani, Haresh, et al.
Publicado: (2017) -
Ligand Pose and Orientational Sampling in Molecular Docking
por: Coleman, Ryan G., et al.
Publicado: (2013) -
Protein–Ligand Docking in the Machine-Learning Era
por: Yang, Chao, et al.
Publicado: (2022)