RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. Ho...

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Detalles Bibliográficos
Autores principales: Bertin, Paul, Rector-Brooks, Jarrid, Sharma, Deepak, Gaudelet, Thomas, Anighoro, Andrew, Gross, Torsten, Martínez-Peña, Francisco, Tang, Eileen L., Suraj, M.S., Regep, Cristian, Hayter, Jeremy B.R., Korablyov, Maksym, Valiante, Nicholas, van der Sloot, Almer, Tyers, Mike, Roberts, Charles E.S., Bronstein, Michael M., Lairson, Luke L., Taylor-King, Jake P., Bengio, Yoshua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626197/
https://www.ncbi.nlm.nih.gov/pubmed/37797618
http://dx.doi.org/10.1016/j.crmeth.2023.100599

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626197/
https://www.ncbi.nlm.nih.gov/pubmed/37797618
http://dx.doi.org/10.1016/j.crmeth.2023.100599

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