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A machine learning platform to estimate anti-SARS-CoV-2 activities

preprint
revised on 18.03.2021, 02:26 and posted on 19.03.2021, 04:48 by Govinda KC, Giovanni Bocci, Srijan Verma, Mahmudulla Hassan, Jayme Holmes, Jeremy Yang, suman sirimulla, Tudor I. Oprea

Strategies for drug discovery and repositioning are an urgent 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 the usage of eleven models spanning 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). In addition, the web-app 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.


Funding

Dr. Sirimulla acknowledges support from the National Science Foundation through NSF-PREM grant #DMR-1827745.

The DrugCentral component of this work is funded by NIH Common Fund U24 CA224370.

History

Email Address of Submitting Author

ssirimulla@utep.edu

Institution

The University of Texas at El Paso

Country

United States

ORCID For Submitting Author

0000-0003-4665-6665

Declaration of Conflict of Interest

The authors did not declare any conflicts of interest.

Version Notes

This is latest version at the time of publication acceptance in NMI.

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