An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non-linear Regression Techniques.
ANN
MLR
SVM
guadipyr
pharmacophore
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
18
09
2018
accepted:
04
12
2018
pubmed:
12
1
2019
medline:
2
10
2020
entrez:
12
1
2019
Statut:
ppublish
Résumé
Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC
Identifiants
pubmed: 30632677
doi: 10.1002/minf.201800119
doi:
Substances chimiques
Guadipyr
0
Guanidines
0
Insecticides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1800119Informations de copyright
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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