In silico prediction of mosquito repellents for clothing application.
Aedes aegypti
Repellent
SAR
artificial neural network
clothing
Journal
SAR and QSAR in environmental research
ISSN: 1029-046X
Titre abrégé: SAR QSAR Environ Res
Pays: England
ID NLM: 9440156
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
entrez:
9
5
2022
pubmed:
10
5
2022
medline:
12
5
2022
Statut:
ppublish
Résumé
Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against
Identifiants
pubmed: 35532305
doi: 10.1080/1062936X.2022.2062871
doi:
Substances chimiques
Insect Repellents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM