配体(生物化学)
化学
受体
对接(动物)
反激动剂
兴奋剂
部分激动剂
虚拟筛选
肾上腺素能受体
G蛋白偶联受体
计算机科学
药物发现
深度学习
药效团
作者
Katherine J. Schultz,Sean M. Colby,Vivian S. Lin,Aaron T. Wright,Ryan S. Renslow
标识
DOI:10.1021/acs.jcim.0c01019
摘要
The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.
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