Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
National Institute of Mental Health, Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599, United States.
J Chem Inf Model. 2023 Aug 28;63(16):5056-5065. doi: 10.1021/acs.jcim.3c00651. Epub 2023 Aug 9.
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 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.
可能有效的治疗阿片成瘾的药物干预措施包括试图在戒断期间减轻大脑奖赏缺陷。κ-阿片受体（KOR）的药理学阻断已被证明可以在戒断期间消除啮齿动物的大脑奖赏缺陷，并减少具有延长阿片类药物接触的大鼠中阿片类药物使用的升级。尽管 KOR 拮抗剂是治疗阿片类药物成瘾的有前途的候选药物，但迄今为止，只有极少数有效的选择性 KOR 拮抗剂被发现，其中大多数存在显著的安全问题。在这里，我们使用了一种生成式深度学习框架来设计具有潜在 KOR 拮抗活性的化学型。基于与受体的预测最佳相互作用，根据模型预测的最佳相互作用对该框架训练的分子进行化学合成的优先级排序。我们的模型和提出的训练方案通过结合和功能测定进行了实验验证。
