| Autor: |
Salas-Estrada L, Provasi D, Qui X, Kaniskan HÜ, Huang XP, DiBerto JF, Ribeiro JML, Jin J, Roth BL, Filizola M |
| Jazyk: |
angličtina |
| Zdroj: |
BioRxiv : the preprint server for biology [bioRxiv] 2023 Apr 26. Date of Electronic Publication: 2023 Apr 26. |
| DOI: |
10.1101/2023.04.25.537995 |
| Abstrakt: |
Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits 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 significant 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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