Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads
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We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC50 < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC50 = 4.53 μM) and the anticancer clinical candidate lucanthone (IC50 = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future.
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Bayesian ML was employed to build predictive data learning models for identifying compounds active against the Ebola virus.
“Moreover, Bayesian ML was employed in [20] to build predictive data learning models for identifying compounds active against the Ebola virus.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [20]
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Research works printed but not linked: 1
- Johansen L. M.; Brannan J. M.; Delos S. E.; Shoemaker C. J.; Stossel A.; Lear C.; Hoffstrom B. G.; DeWald L. E.; Schornberg K. L.; Scully C.; Lehár J.; Hensley L. E.; White J. M.; Olinger G. G. FDA-approved selective estrogen receptor modulators inhibit Ebola virus infection. Sci. Transl. Med. 2013, 5, 190ra79 10.1126/scitranslmed.3005471. PMC3955358 23785035 · DOI
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- Authors
- Anantpadma, M., Lane, T., Zorn, K. M., Lingerfelt, M. A., Clark, A. M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S.
- Affiliations
- Texas Biomedical Research Institute (US)
- Collaborations Pharmaceuticals (United States) (US)
- Molecular Materials Informatics, Inc. , 1900 St. Jacques #302 , , ,
- Rutgers, The State University of New Jersey (US)
- SRI International (US)
- Corresponding author
- Ekins, Sean
- ORCID
- Venue
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-01-30
- Volume
- 4
- Issue
- 1
- Pages
- 2353-2361
- ISSN
- 2470-1343 (ISSN-L)
- PMC
- PMC6356859
- OpenAlex
- W2913529882
- Cited by
- 54
- References
- 80
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 8 · 2022: 8 · 2021: 14 · 2020: 16 · 2019: 3
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ACS Omega (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Center for Advancing Translational Sciences (R21 TR001718)
- National Institute of General Medical Sciences (R44 GM122196-02A1)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- Ebola virus, Bayesian learning, antiviral drug discovery, in vitro drug testing, tilorone, pyronaridine, quinacrine, lucanthone, antimalarial compounds, compound prioritization, HeLa cells, cytotoxicity
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