Splitting chemical structure data sets for federated privacy-preserving machine learning

With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and tes...

Descripción completa

Detalles Bibliográficos
Autores principales: Simm, Jaak, Humbeck, Lina, Zalewski, Adam, Sturm, Noe, Heyndrickx, Wouter, Moreau, Yves, Beck, Bernd, Schuffenhauer, Ansgar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650276/
https://www.ncbi.nlm.nih.gov/pubmed/34876230
http://dx.doi.org/10.1186/s13321-021-00576-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650276/
https://www.ncbi.nlm.nih.gov/pubmed/34876230
http://dx.doi.org/10.1186/s13321-021-00576-2

Ejemplares similares