Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved]
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 inf...
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| Format: | Article |
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F1000 Research Ltd
2016-01-01
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| Online Access: | http://f1000research.com/articles/4-1091/v2 |
| _version_ | 1818133441085964288 |
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| author | Sean Ekins Joel S. Freundlich Alex M. Clark Manu Anantpadma Robert A. Davey Peter Madrid |
| author_facet | Sean Ekins Joel S. Freundlich Alex M. Clark Manu Anantpadma Robert A. Davey Peter Madrid |
| author_sort | Sean Ekins |
| collection | DOAJ |
| description | 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 EC50 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. |
| first_indexed | 2024-12-11T08:52:46Z |
| format | Article |
| id | doaj.art-67d44938cd3d4322910de02c65d19e70 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-11T08:52:46Z |
| publishDate | 2016-01-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-67d44938cd3d4322910de02c65d19e702022-12-22T01:13:58ZengF1000 Research LtdF1000Research2046-14022016-01-01410.12688/f1000research.7217.28235Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved]Sean Ekins0Joel S. Freundlich1Alex M. Clark2Manu Anantpadma3Robert A. Davey4Peter Madrid5Collaborative Drug Discovery, Burlingame, CA, 94010, USADepartments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ, New Jersey Medical School, Newark, NJ, 07103, USAMolecular Materials Informatics, Inc., Montreal, 94025, CanadaTexas Biomedical Research Institute, San Antonio, TX, 78227, USATexas Biomedical Research Institute, San Antonio, TX, 78227, USASRI International, Menlo Park, CA, 94025, USAThe 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 EC50 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.http://f1000research.com/articles/4-1091/v2Drug Discovery & DesignSmall Molecule ChemistryViral Infections (without HIV)Virology |
| spellingShingle | Sean Ekins Joel S. Freundlich Alex M. Clark Manu Anantpadma Robert A. Davey Peter Madrid Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] F1000Research Drug Discovery & Design Small Molecule Chemistry Viral Infections (without HIV) Virology |
| title | Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] |
| title_full | Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] |
| title_fullStr | Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] |
| title_full_unstemmed | Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] |
| title_short | Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved] |
| title_sort | machine learning models identify molecules active against the ebola virus in vitro version 2 referees 2 approved |
| topic | Drug Discovery & Design Small Molecule Chemistry Viral Infections (without HIV) Virology |
| url | http://f1000research.com/articles/4-1091/v2 |
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