Cargando…
Traditional Machine and Deep Learning for Predicting Toxicity Endpoints
Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for app...
Autor principal: | Norinder, Ulf |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822478/ https://www.ncbi.nlm.nih.gov/pubmed/36615411 http://dx.doi.org/10.3390/molecules28010217 |
Ejemplares similares
-
hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques
por: Ylipää, Erik, et al.
Publicado: (2023) -
Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning
por: Norinder, Ulf, et al.
Publicado: (2021) -
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
por: Wilm, Anke, et al.
Publicado: (2021) -
Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques
por: Kurt, Burçin, et al.
Publicado: (2023) -
Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses
por: Wang, Hao, et al.
Publicado: (2019)