Cargando…
Generating 3D molecules conditional on receptor binding sites with deep generative models
The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by samp...
Autores principales: | Ragoza, Matthew, Masuda, Tomohide, Koes, David Ryan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890264/ https://www.ncbi.nlm.nih.gov/pubmed/35356675 http://dx.doi.org/10.1039/d1sc05976a |
Ejemplares similares
-
GNINA 1.0: molecular docking with deep learning
por: McNutt, Andrew T., et al.
Publicado: (2021) -
Computational Discovery of TTF Molecules with Deep Generative Models
por: Yakubovich, Alexander, et al.
Publicado: (2021) -
Deep generative design with 3D pharmacophoric constraints
por: Imrie, Fergus, et al.
Publicado: (2021) -
Structure-based de novo drug design using 3D deep generative models
por: Li, Yibo, et al.
Publicado: (2021) -
Potent mechanism-based sirtuin-2-selective inhibition by an in situ-generated occupant of the substrate-binding site, “selectivity pocket” and NAD(+)-binding site
por: Mellini, Paolo, et al.
Publicado: (2017)