scholarly journals Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

ACS Omega ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 2353-2361 ◽  
Author(s):  
Manu Anantpadma ◽  
Thomas Lane ◽  
Kimberley M. Zorn ◽  
Mary A. Lingerfelt ◽  
Alex M. Clark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ebola Virus ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2017 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

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.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

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.

scholarly journals Machine Learning Models Identify Inhibitors of SARS-CoV-2

2020 ◽  
Author(s):  
Victor O. Gawriljuk ◽  
Phyo Phyo Kyaw Zin ◽  
Daniel H. Foil ◽  
Jean Bernatchez ◽  
Sungjun Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Published Data ◽  
Learning Models ◽  
Machine Learning Model ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

AbstractWith the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

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.

scholarly journals Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145564-145576 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi Hosseini ◽  
Bahram Choubin ◽  
Massoud Goodarzi ◽  
Adrienn A. Dineva
Keyword(s):  
Machine Learning ◽  
Susceptibility Mapping ◽  
Classifier Ensemble ◽  
Groundwater Salinity ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laura-Jayne Gardiner ◽  
Anna Paola Carrieri ◽  
Jenny Wilshaw ◽  
Stephen Checkley ◽  
Edward O. Pyzer-Knapp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transcriptome Analysis ◽  
Drug Toxicity ◽  
Learning Models ◽  
Machine Learning Models
Sign in / Sign up

Export Citation Format

Share Document