Dissecting celastrol with machine learning to unveil dark pharmacology
Accepted version
Peer-reviewed
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Authors
Rodrigues, Tiago
de Almeida, Bernardo P
Barbosa-Morais, Nuno L
Bernardes, Gonçalo JL
Abstract
Coallescing bespoke machine learning and bioinformatics analyses with cell-based assays we unveil pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the antiproliferative activity. We demonstrate how in silico methods may drive systems biology studies for natural products.
Description
Keywords
Cell Proliferation, Computational Biology, Humans, Machine Learning, Pentacyclic Triterpenes, Progesterone, Receptors, Cannabinoid, Systems Biology, Triterpenes
Journal Title
Chemical Communications
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Journal ISSN
1359-7345
1364-548X
1364-548X
Volume Title
Publisher
Royal Society of Chemistry (RSC)
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All rights reserved
Sponsorship
European Research Council (676832)
Royal Society (URF\R\180019)
European Commission Horizon 2020 (H2020) Spreading Excellence and Widening Participation (807281)
Royal Society (URF\R\180019)
European Commission Horizon 2020 (H2020) Spreading Excellence and Widening Participation (807281)
We thank the Royal Society (URF\R\180019), H2020 (ERC StG, GA no. 676832, 743640 ad 807281) and FCT Portugal (IF/00624/2015 and 02/SAICT/2017, Grant 28333) for funding.