Cargando…
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints...
Autores principales: | Wilm, Anke, Garcia de Lomana, Marina, Stork, Conrad, Mathai, Neann, Hirte, Steffen, Norinder, Ulf, Kühnl, Jochen, Kirchmair, Johannes |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402010/ https://www.ncbi.nlm.nih.gov/pubmed/34451887 http://dx.doi.org/10.3390/ph14080790 |
Ejemplares similares
-
Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules
por: Wilm, Anke, et al.
Publicado: (2020) -
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability
por: Wilm, Anke, et al.
Publicado: (2019) -
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
por: Mathai, Neann, et al.
Publicado: (2021) -
NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules
por: Chen, Ya, et al.
Publicado: (2019) -
Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope
por: Mathai, Neann, et al.
Publicado: (2020)