Synthesis of environmentally friendly neonicotinoid insecticide with proper functional properties by theoretical methodologies.

Density functional theory Neonicotinoid insecticides substitute Positive and unlabeled machine learning Synthesis prediction Time-dependent density functional theory

Journal

Ecotoxicology and environmental safety
ISSN: 1090-2414
Titre abrégé: Ecotoxicol Environ Saf
Pays: Netherlands
ID NLM: 7805381

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 19 07 2023
revised: 08 11 2023
accepted: 14 11 2023
medline: 4 12 2023
pubmed: 19 11 2023
entrez: 18 11 2023
Statut: ppublish

Résumé

Modern insecticide substitutes using acetylcholine receptors (nAChR) as biochemical targets, such as neonicotinoid insecticides (NNIs), have been extensively researched. Only 12 compounds have been experimentally realized since the initial discovery of imidacloprid. Increasingly, the bottleneck in this field is to rapidly determine the synthesizability of NNI substitutes. Here, we designed a coupled evaluation system for synthesis prediction and validation, including the synthesis probability, reaction path difficulty, and electron transfer characteristics of NNIs and their substitutes. Firstly, a total of 1475 eigenvalues were generated and 52 critical eigenvalues were screened out through the Pearson's correlation coefficient. The positive and unlabeled (PU) machine learning was constructed using the critical eigenvalues NNIs, including 12 experimentally synthesized NNIs (positive samples) and 73 unsynthesized NNI substitutes (unlabeled samples). Results identified 3 NNI substitutes that were highly promising candidates for synthesis (synthesis probability > 0.5). The results of density functional theory demonstrated the ranking of their reaction ease was UN-1 (31.4 kcal/mol) > UN-2 (81.6 kcal/mol) > UN-3 (3.35 ×10

Identifiants

pubmed: 37979357
pii: S0147-6513(23)01212-5
doi: 10.1016/j.ecoenv.2023.115708
pii:
doi:

Substances chimiques

Insecticides 0
Neonicotinoids 0
Nitro Compounds 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

115708

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yuanyuan Zhao (Y)

College of New Energy and Environment, Jilin University, Changchun 130012, China; College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China.

Hao Yang (H)

College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China.

Yanbing Chen (Y)

College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China.

Meijin Du (M)

College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China.

Wenwen Gu (W)

College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China.

Wenjin Zhao (W)

College of New Energy and Environment, Jilin University, Changchun 130012, China. Electronic address: zhaowj@jlu.edu.cn.

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Classifications MeSH