학술논문

Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models.
Document Type
Article
Source
SAR & QSAR in Environmental Research. Sep2022, Vol. 33 Issue 9, p729-751. 23p.
Subject
*HUMAN fingerprints
*ARTIFICIAL neural networks
*REPELLENTS
*AEDES aegypti
*MOSQUITOES
Language
ISSN
1062-936X
Abstract
Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors. [ABSTRACT FROM AUTHOR]