Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking

Opioids represent a highly-abused and highly potent class of drugs that have become a significant threat to public safety. Often there are little to no pharmacological and toxicological data available for new, illicitly used and abused opioids, and this has resulted in a growing number of serious ad...

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Bibliographic Details
Published in:	PloS one Vol. 13; no. 5; p. e0197734
Main Authors:	Ellis, Christopher R., Kruhlak, Naomi L., Kim, Marlene T., Hawkins, Edward G., Stavitskaya, Lidiya
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 24.05.2018 Public Library of Science (PLoS)
Subjects:	60 APPLIED LIFE SCIENCES Affinity Analgesics Analogs Analysis Binding Binding Sites Biology and Life Sciences Care and treatment Computer applications Correlation analysis Derivatives Dosage and administration Drug abuse Drug overdose Fentanyl Fentanyl - analogs & derivatives Fentanyl - chemistry Fentanyl - metabolism Food Health risks Humans Kinetics Mathematical models Medicine and Health Sciences Molecular chains Molecular docking Molecular Docking Simulation Narcotics Opioid receptors (type mu) Opioids Pain Pharmacology Physical Sciences Protein Binding Protein Structure, Tertiary Public health Public safety Receptors, Opioid, mu - chemistry Receptors, Opioid, mu - metabolism Risk factors Safety Safety and security measures Schedules Science & Technology - Other Topics Substance abuse Substance abuse treatment Thermodynamics Canada United States > US Maryland
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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