A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their gr...

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Detalles Bibliográficos
Autores principales: Rahman, A. S. M. Zisanur, Liu, Chengyou, Sturm, Hunter, Hogan, Andrew M., Davis, Rebecca, Hu, Pingzhao, Cardona, Silvia T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9624395/
https://www.ncbi.nlm.nih.gov/pubmed/36228001
http://dx.doi.org/10.1371/journal.pcbi.1010613

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9624395/
https://www.ncbi.nlm.nih.gov/pubmed/36228001
http://dx.doi.org/10.1371/journal.pcbi.1010613

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