Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.25.537995v1?rss=1
Authors: Salas-Estrada, L., Provasi, D., Qiu, X., Kaniskan, H. U., Huang, X.-P., DiBerto, J., Marcelo Lamim Ribeiro, J., Jin, J., Roth, B. L., Filizola, M.
Abstract:
Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward de[fi]cits during periods of abstinence. Pharmacological blockade of the {kappa}-opioid receptor (KOR) has been shown to abolish brain reward de[fi]cits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit signi[fi]cant safety concerns. Here, we used a generative deep learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.
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