Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists-Reference-Cited by-同舟云学术

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

Published:2021-01-06 Issue:1 Volume:61 Page:481-492
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Schultz Katherine J.¹,Colby Sean M.¹,Lin Vivian S.¹^ORCID,Wright Aaron T.¹²^ORCID,Renslow Ryan S.¹²^ORCID

Affiliation:

1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

2. The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99163, United States

Funder

Laboratory Directed Research and Development

Defense Threat Reduction Agency

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c01019

Reference90 articles.

1. The druggable genome

2. Alpha-2 Adrenergic Receptor Agonists: A Review of Current Clinical Applications

3. Structural determinants of the alpha2 adrenoceptor subtype selectivity

4. Synthesis and in vivo evaluation of [11C]MPTQ: A potential PET tracer for alpha2A-adrenergic receptors

5. Structure-based drug screening for G-protein-coupled receptors

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