Title:

Regression quantitative structure-toxicity relationship of pesticides on fishes

Alternative title:

Regresja ilościowa związku struktura-toksyczność pestycydów na rybach

Creator:

Yang, Bowen ; Dang, Limin ; Chen, Cong ; Li, Mingwang

Subject:

toxicity ; pesticide ; QSTR ; general regression neural network

Description:

Pesticide usage reaches several million metric tons annually worldwide, and the effects of pesticides on non-target species, such as various fishes in aquatic environments, have resulted in serious concerns. Predicting pesticide aquatic toxicity to fish is of great significance. In this paper, 20 molecular descriptors were successfully used to develop a regression quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity logLC50 of a large data set consisting of 1106 pesticides on fishes by using a general regression neural network (GRNN) algorithm. The optimal GRNN model produced correlation coefficients R of 0.8901 (rms = 0.6910) for the training set, 0.8531 (rms = 0.7486) for the validation set, and 0.8802 (rms = 0.6903) for the test set, which are satisfactory compared with other models in the literature, although a large data set of toxicity logLC50 was used in this work.

Place of publishing:

Koszalin

Publisher:

Politechnika Koszalińska

Date:

2024

Type:

artykuł w czasopiśmie

Language:

eng

Is part of:

Rocznik Ochrona Środowiska. Vol. 26, s. 264-272

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY-SA 4.0