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

Schultz, Katherine J.; Colby, Sean M.; Lin, Vivian S.; Wright, Aaron T.; Renslow, Ryan S.

doi:10.1021/acs.jcim.0c01019

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

Journal Article · Wed Jan 06 00:00:00 EST 2021 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.0c01019· OSTI ID:1763358

Schultz, Katherine J. ^[1]; Colby, Sean M. ^[1];

^[1];

^[2];

^[2]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Pullman, WA (United States)

The α2a adrenoceptor is a particularly medically relevant subtype of the G protein-couple receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads targeted to this receptor have been largely unsuccessful due to 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 insight into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. Additionally, we further describe how such assessment techniques can be applied to putative chemical probes with a case study involved proposed medetomidine-based probes.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; Defense Threat Reduction Agency, Chemical and Biological Technologies Dept.

Grant/Contract Number:: AC05-76RL01830; HDTRA1033013

OSTI ID:: 1763358

Report Number(s):: PNNL-SA-155859

Journal Information:: Journal of Chemical Information and Modeling, Vol. 61, Issue 1; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Agonists
ligands
drug discovery
molecules
receptors

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

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