Cargando…
MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database
The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are...
Autores principales: | Mazzolari, Angelica, Perazzoni, Pietro, Sabato, Emanuela, Lunghini, Filippo, Beccari, Andrea R., Vistoli, Giulio, Pedretti, Alessandro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341931/ https://www.ncbi.nlm.nih.gov/pubmed/37446241 http://dx.doi.org/10.3390/ijms241311064 |
Ejemplares similares
-
MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database
por: Mazzolari, Angelica, et al.
Publicado: (2021) -
Ensemble of structure and ligand-based classification models for hERG liability profiling
por: Vittorio, Serena, et al.
Publicado: (2023) -
MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation
por: Mazzolari, Angelica, et al.
Publicado: (2021) -
Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program
por: Mazzolari, Angelica, et al.
Publicado: (2018) -
Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
por: Pedretti, Alessandro, et al.
Publicado: (2019)