Abstract | AIMS: Over the past few years, AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process. The purpose of our study was to identify the best repurposing candidates among FDA-approved drugs, based on their predicted antiviral activity against SARS-CoV-2. MATERIALS AND METHODS: This article describes a drug discovery screening based on a supervised machine learning model, trained on in vitro data encoded in chemical fingerprints, representing particular molecular substructures. Predictive performance of our model has been evaluated using so-called scaffold splits offering a state-of-the-art setup for assessing model's ability to generalize to new chemical spaces, critical for drug repurposing applications. KEY FINDINGS: SIGNIFICANCE:
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Authors | Marcin Delijewski, Jacek Haneczok |
Journal | Medicine in drug discovery
(Med Drug Discov)
Vol. 9
Pg. 100077
(Mar 2021)
ISSN: 2590-0986 [Electronic] Netherlands |
PMID | 33521623
(Publication Type: Journal Article)
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Copyright | © 2020 The Authors. |