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Title: A machine learning platform to estimate anti-SARS-CoV-2 activities
Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and use of eleven models that span across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal (http://drugcentral.org/Redial). The web application also provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment.  more » « less
Award ID(s):
1827745
PAR ID:
10227617
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Nature Machine Intelligence
ISSN:
2522-5839
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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