Literature DB >> 26911563

QSAR study of the acute toxicity to fathead minnow based on a large dataset.

X Wu1, Q Zhang1, J Hu1.   

Abstract

Acute fathead minnow toxicity is an important basis of hazard and risk assessment for compounds in the aquatic environment. In this paper, a large dataset consisting of 963 organic compounds with acute toxicity towards fathead minnow was studied with a QSAR approach. All molecular structures of compounds were optimized by the hybrid density functional theory method. Dragon molecular descriptors and log Kow were selected to describe molecular information. Genetic algorithm and multiple linear regression analysis were combined to develop models. A global prediction model for compounds without known mode of action and two local models for organic compounds that exhibit narcosis toxicity and excess toxicity were developed, respectively. For all developed models, internal validations were performed by cross-validation and external validations were implemented by the setting of validation set. In addition, applicability domains of models were evaluated using a leverage method and outliers were listed and checked using toxicological knowledge.

Entities:  

Keywords:  Aquatic organisms; GA-MLR; QSAR; global model; local model

Mesh:

Substances:

Year:  2016        PMID: 26911563     DOI: 10.1080/1062936X.2015.1137353

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  7 in total

1.  QSAR model for predicting the toxicity of organic compounds to fathead minnow.

Authors:  Qingzhu Jia; Yunpeng Zhao; Fangyou Yan; Qiang Wang
Journal:  Environ Sci Pollut Res Int       Date:  2018-10-22       Impact factor: 4.223

2.  Combined molecular docking, homology modelling and density functional theory studies to modify dioxygenase to efficiently degrade aromatic hydrocarbons.

Authors:  Xingchun Li; Zhenhua Chu; Xianyuan Du; Youli Qiu; Yu Li
Journal:  RSC Adv       Date:  2019-04-11       Impact factor: 4.036

3.  In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

Authors:  Guohui Sun; Tengjiao Fan; Xiaodong Sun; Yuxing Hao; Xin Cui; Lijiao Zhao; Ting Ren; Yue Zhou; Rugang Zhong; Yongzhen Peng
Journal:  Molecules       Date:  2018-11-06       Impact factor: 4.411

4.  A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling.

Authors:  Yukun Wang; Xuebo Chen
Journal:  RSC Adv       Date:  2020-06-04       Impact factor: 4.036

5.  Machine learning-based prediction of toxicity of organic compounds towards fathead minnow.

Authors:  Xingmei Chen; Limin Dang; Hai Yang; Xianwei Huang; Xinliang Yu
Journal:  RSC Adv       Date:  2020-10-01       Impact factor: 4.036

6.  Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity.

Authors:  Gulnara Shavalieva; Stavros Papadokonstantakis; Gregory Peters
Journal:  J Chem Inf Model       Date:  2022-08-23       Impact factor: 6.162

7.  QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds.

Authors:  Tengjiao Fan; Guohui Sun; Lijiao Zhao; Xin Cui; Rugang Zhong
Journal:  Int J Mol Sci       Date:  2018-10-03       Impact factor: 5.923

  7 in total

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