Cargando…
SyntaLinker: automatic fragment linking with deep conditional transformer neural networks
Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural...
Autores principales: | Yang, Yuyao, Zheng, Shuangjia, Su, Shimin, Zhao, Chao, Xu, Jun, Chen, Hongming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163338/ https://www.ncbi.nlm.nih.gov/pubmed/34123096 http://dx.doi.org/10.1039/d0sc03126g |
Ejemplares similares
-
Deep scaffold hopping with multimodal transformer neural networks
por: Zheng, Shuangjia, et al.
Publicado: (2021) -
Chicken or the egg: ST elevation in lead aVR or SYNTA X score
por: Cerit, Levent
Publicado: (2017) -
Blockage of Store-Operated Ca(2+) Influx by Synta66 is Mediated by Direct Inhibition of the Ca(2+) Selective Orai1 Pore
por: Waldherr, Linda, et al.
Publicado: (2020) -
Fragment Linker
Prediction Using the Deep Encoder-Decoder
Network for PROTACs Drug Design
por: Kao, Chien-Ting, et al.
Publicado: (2023) -
MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching
por: Loureiro, Daniel, et al.
Publicado: (2020)