Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks (GNNs), have shown exceptionally good performance...

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Detalles Bibliográficos
Autores principales: Wieder, Oliver, Kuenemann, Mélaine, Wieder, Marcus, Seidel, Thomas, Meyer, Christophe, Bryant, Sharon D., Langer, Thierry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539502/
https://www.ncbi.nlm.nih.gov/pubmed/34684766
http://dx.doi.org/10.3390/molecules26206185

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539502/
https://www.ncbi.nlm.nih.gov/pubmed/34684766
http://dx.doi.org/10.3390/molecules26206185

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