Cargando…
Quantitative prediction of selectivity between the A(1) and A(2A) adenosine receptors
The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (ind...
Autores principales: | Burggraaff, Lindsey, van Vlijmen, Herman W. T., IJzerman, Adriaan P., van Westen, Gerard J. P. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222572/ https://www.ncbi.nlm.nih.gov/pubmed/33431012 http://dx.doi.org/10.1186/s13321-020-00438-3 |
Ejemplares similares
-
An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A(2A) receptor
por: Liu, Xuhan, et al.
Publicado: (2019) -
Annotation of Allosteric Compounds to Enhance Bioactivity
Modeling for Class A GPCRs
por: Burggraaff, Lindsey, et al.
Publicado: (2020) -
Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling
por: Burggraaff, Lindsey, et al.
Publicado: (2019) -
Deciphering conformational selectivity in the A(2A) adenosine G protein-coupled receptor by free energy simulations
por: Jespers, Willem, et al.
Publicado: (2021) -
Significantly Improved HIV Inhibitor Efficacy Prediction Employing Proteochemometric Models Generated From Antivirogram Data
por: van Westen, Gerard J. P., et al.
Publicado: (2013)