Abstract：
: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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Schultz KJ,Colby SM,Lin VS,Wright AT,Renslow RSdoi
10.1021/acs.jcim.0c01019subject
Has Abstractpub_date
2021-01-25 00:00:00pages
481-492issue
1eissn
1549-9596issn
1549-960Xjournal_volume
61pub_type
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