High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis.

Autor: Xu M; Statistics and Data Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA.; Center for Advanced Turbomachinery and Energy Research, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA., Wang CH; NanoScience Technology Center, University of Central Florida, 12424 Research Parkway, Orlando, FL, 32826, USA., Terracciano AC; Mechanical and Aerospace Engineering, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA.; Center for Advanced Turbomachinery and Energy Research, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA., Masunov AE; NanoScience Technology Center, University of Central Florida, 12424 Research Parkway, Orlando, FL, 32826, USA.; School of Modeling, Simulation, and Training, University of Central Florida, 3100 Technology Parkway, Orlando, FL, 32816, USA.; Department of Chemistry, University of Central Florida, 4111 Libra Dr., Orlando, FL, 32816, USA.; South Ural State University, Lenin pr. 76, Chelyabinsk, 454080, Russia.; National Research Nuclear University MEPhI, Kashirskoye shosse 31, Moscow, 115409, Russia., Vasu SS; Mechanical and Aerospace Engineering, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA. subith@ucf.edu.; Center for Advanced Turbomachinery and Energy Research, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA. subith@ucf.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2020 Aug 11; Vol. 10 (1), pp. 13569. Date of Electronic Publication: 2020 Aug 11.
DOI: 10.1038/s41598-020-70471-7
Abstrakt: Fentanyl is an anesthetic with a high bioavailability and is the leading cause of drug overdose death in the U.S. Fentanyl and its derivatives have a low lethal dose and street drugs which contain such compounds may lead to death of the user and simultaneously pose hazards for first responders. Rapid identification methods of both known and emerging opioid fentanyl substances is crucial. In this effort, machine learning (ML) is applied in a systematic manner to identify fentanyl-related functional groups in such compounds based on their observed spectral properties. In our study, accurate infrared (IR) spectra of common organic molecules which contain functional groups that are constituents of fentanyl is determined by investigating the structure-property relationship. The average accuracy rate of correctly identifying the functional groups of interest is 92.5% on our testing data. All the IR spectra of 632 organic molecules are from National Institute of Standards and Technology (NIST) database as the training set and are assessed. Results from this work will provide Artificial Intelligence (AI) based tools and algorithms increased confidence, which serves as a basis to detect fentanyl and its derivatives.
Databáze: MEDLINE
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