Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists

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.