Cargando…
Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing
Retrosynthesis planning, the process of identifying a set of available reactions to synthesize the target molecules, remains a major challenge in organic synthesis. Recently, computer-aided synthesis planning has gained renewed interest and various retrosynthesis prediction algorithms based on deep...
Autores principales: | Zhong, Weihe, Yang, Ziduo, Chen, Calvin Yu-Chian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209957/ https://www.ncbi.nlm.nih.gov/pubmed/37230985 http://dx.doi.org/10.1038/s41467-023-38851-5 |
Ejemplares similares
-
Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
por: Yang, Ziduo, et al.
Publicado: (2022) -
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
por: Yang, Ziduo, et al.
Publicado: (2022) -
G(2)Retro as a two-step graph generative models for retrosynthesis prediction
por: Chen, Ziqi, et al.
Publicado: (2023) -
Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning
por: Zhang, Baicheng, et al.
Publicado: (2022) -
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction
por: Yang, Yang, et al.
Publicado: (2021)