Cargando…
A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation
The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement o...
Autores principales: | Koutroumpa, Nikoletta-Maria, Papavasileiou, Konstantinos D., Papadiamantis, Anastasios G., Melagraki, Georgia, Afantitis, Antreas |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095548/ https://www.ncbi.nlm.nih.gov/pubmed/37047543 http://dx.doi.org/10.3390/ijms24076573 |
Ejemplares similares
-
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
por: Mouchlis, Varnavas D., et al.
Publicado: (2021) -
Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein
por: Moschovou, Kalliopi, et al.
Publicado: (2023) -
Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials
por: Saarimäki, Laura Aliisa, et al.
Publicado: (2021) -
Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics
por: Mouchlis, Varnavas D., et al.
Publicado: (2020) -
Open Source Chemoinformatics Software including KNIME Analytics
por: Leonis, Georgios, et al.
Publicado: (2016)