Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data

We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as m...

Descripción completa

Detalles Bibliográficos
Autores principales: Ståhl, Niclas, Falkman, Göran, Karlsson, Alexander, Mathiason, Gunnar, Boström, Jonas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2018
Materias:
Workshops
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798861/
https://www.ncbi.nlm.nih.gov/pubmed/30517077
http://dx.doi.org/10.1515/jib-2018-0065

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798861/
https://www.ncbi.nlm.nih.gov/pubmed/30517077
http://dx.doi.org/10.1515/jib-2018-0065

Ejemplares similares