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

Abstract

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.

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
+ Show Author Affiliations
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Pullman, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; Defense Threat Reduction Agency, Chemical and Biological Technologies Dept.
OSTI Identifier:
1763358
Report Number(s):
PNNL-SA-155859
Journal ID: ISSN 1549-9596
Grant/Contract Number:  
AC05-76RL01830; HDTRA1033013
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 61; Journal Issue: 1; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Agonists; ligands; drug discovery; molecules; receptors

Citation Formats

Schultz, Katherine J., Colby, Sean M., Lin, Vivian S., Wright, Aaron T., and Renslow, Ryan S. Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists. United States: N. p., 2021. Web. doi:10.1021/acs.jcim.0c01019.
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Schultz, Katherine J., Colby, Sean M., Lin, Vivian S., Wright, Aaron T., & Renslow, Ryan S. Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists. United States. https://doi.org/10.1021/acs.jcim.0c01019
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Schultz, Katherine J., Colby, Sean M., Lin, Vivian S., Wright, Aaron T., and Renslow, Ryan S. Wed . "Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists". United States. https://doi.org/10.1021/acs.jcim.0c01019. https://www.osti.gov/servlets/purl/1763358.
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@article{osti_1763358,
title = {Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists},
author = {Schultz, Katherine J. and Colby, Sean M. and Lin, Vivian S. and Wright, Aaron T. and Renslow, Ryan S.},
abstractNote = {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.},
doi = {10.1021/acs.jcim.0c01019},
journal = {Journal of Chemical Information and Modeling},
number = 1,
volume = 61,
place = {United States},
year = {Wed Jan 06 00:00:00 EST 2021},
month = {Wed Jan 06 00:00:00 EST 2021}
}
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https://doi.org/10.1021/acs.jcim.0c01019
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