Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
Abstract We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simul...
Guardado en:
Autores principales: | Namid R. Stillman, Igor Balaz, Michail-Antisthenis Tsompanas, Marina Kovacevic, Sepinoud Azimi, Sébastien Lafond, Andrew Adamatzky, Sabine Hauert |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/49ab49b7d5c1485abf5565b9443d2620 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning for perovskite materials design and discovery
por: Qiuling Tao, et al.
Publicado: (2021) -
Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications
por: Yangyang Wan, et al.
Publicado: (2021) -
High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts
por: Byung Chul Yeo, et al.
Publicado: (2021) -
Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
por: Zhilong Wang, et al.
Publicado: (2021) -
Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
por: Pikee Priya, et al.
Publicado: (2021)