Cargando…
Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space
Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of ‘near-drug’ properties is still a challenging task. In this work, we proposed a multimodal c...
Autores principales: | Wang, Jie, Shen, Zihao, Liao, Yichen, Yuan, Zhen, Li, Shiliang, He, Gaoqi, Lan, Man, Qian, Xuhong, Zhang, Kai, Li, Honglin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677486/ https://www.ncbi.nlm.nih.gov/pubmed/36252922 http://dx.doi.org/10.1093/bib/bbac461 |
Ejemplares similares
-
e-TSN: an interactive visual exploration platform for target–disease knowledge mapping from literature
por: Feng, Ziyan, et al.
Publicado: (2022) -
Matching single cells across modalities with contrastive learning and optimal transport
por: Gossi, Federico, et al.
Publicado: (2023) -
bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data
por: Moutsopoulos, Ilias, et al.
Publicado: (2022) -
AI for predicting chemical-effect associations at the chemical universe level—deepFPlearn
por: Schor, Jana, et al.
Publicado: (2022) -
Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies
por: Yeung, Wayland, et al.
Publicado: (2023)