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Machine Learning for Discovery of GSK3β Inhibitors

[Image: see text] Alzheimer’s disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative,...

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Autores principales: Vignaux, Patricia A., Minerali, Eni, Foil, Daniel H., Puhl, Ana C., Ekins, Sean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581251/
https://www.ncbi.nlm.nih.gov/pubmed/33110983
http://dx.doi.org/10.1021/acsomega.0c03302
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author Vignaux, Patricia A.
Minerali, Eni
Foil, Daniel H.
Puhl, Ana C.
Ekins, Sean
author_facet Vignaux, Patricia A.
Minerali, Eni
Foil, Daniel H.
Puhl, Ana C.
Ekins, Sean
author_sort Vignaux, Patricia A.
collection PubMed
description [Image: see text] Alzheimer’s disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine–threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine–threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC(50) = 97.3 nM) and GSK3α (IC(50) = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.
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spelling pubmed-75812512020-10-26 Machine Learning for Discovery of GSK3β Inhibitors Vignaux, Patricia A. Minerali, Eni Foil, Daniel H. Puhl, Ana C. Ekins, Sean ACS Omega [Image: see text] Alzheimer’s disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine–threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine–threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC(50) = 97.3 nM) and GSK3α (IC(50) = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening. American Chemical Society 2020-10-12 /pmc/articles/PMC7581251/ /pubmed/33110983 http://dx.doi.org/10.1021/acsomega.0c03302 Text en © 2020 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Vignaux, Patricia A.
Minerali, Eni
Foil, Daniel H.
Puhl, Ana C.
Ekins, Sean
Machine Learning for Discovery of GSK3β Inhibitors
title Machine Learning for Discovery of GSK3β Inhibitors
title_full Machine Learning for Discovery of GSK3β Inhibitors
title_fullStr Machine Learning for Discovery of GSK3β Inhibitors
title_full_unstemmed Machine Learning for Discovery of GSK3β Inhibitors
title_short Machine Learning for Discovery of GSK3β Inhibitors
title_sort machine learning for discovery of gsk3β inhibitors
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581251/
https://www.ncbi.nlm.nih.gov/pubmed/33110983
http://dx.doi.org/10.1021/acsomega.0c03302
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