Cargando…
Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network
Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limit...
Autores principales: | Du, Hongyan, Jiang, Dejun, Gao, Junbo, Zhang, Xujun, Jiang, Lingxiao, Zeng, Yundian, Wu, Zhenxing, Shen, Chao, Xu, Lei, Cao, Dongsheng, Hou, Tingjun, Pan, Peichen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343084/ https://www.ncbi.nlm.nih.gov/pubmed/35958111 http://dx.doi.org/10.34133/2022/9873564 |
Ejemplares similares
-
MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions
por: Jiang, Dejun, et al.
Publicado: (2023) -
Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
por: Wu, Zhenxing, et al.
Publicado: (2023) -
A flexible data-free framework for structure-based de novo drug design with reinforcement learning
por: Du, Hongyan, et al.
Publicado: (2023) -
CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors
por: Du, Hongyan, et al.
Publicado: (2020) -
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
por: Jiang, Dejun, et al.
Publicado: (2021)