Machine learning models identify molecules active against the Ebola virus in vitro
Open accessAbstract
The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro .
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Our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease and the Ebola virus using Bayesian algorithms.
“Several of our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease13 and the Ebola virus41 using Bayesian algorithms.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [41]
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- Authors
- Ekins, S., Freundlich, J. S., Clark, A. M., Anantpadma, M., Davey, R. A., & Madrid, P.
- Affiliations
- Collaborative Drug Discovery (United States) (US)
- Rutgers New Jersey Medical School (US)
- Texas Biomedical Research Institute (US)
- SRI International (US)
- Corresponding author
- Ekins, Sean
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2015-10-20
- Volume
- 4
- Pages
- 1091
- ISSN
- 2046-1402 (ISSN-L)
- OpenAlex
- W4233790983
- Cited by
- 84
- References
- 74
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 9 · 2022: 9 · 2021: 18 · 2020: 10 · 2019: 11 · 2018: 7 · 2017: 4 · 2016: 5
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Locations
- F1000Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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, antiviral drug discovery, small molecule inhibitors, Bayesian learning, virtual screening, quinacrine, pyronaridine, tilorone, 4-aminoquinolines, antimalarial drugs, drug repurposing
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