QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds

Author(s):  
Uko Maran ◽  
Sulev Sild
2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.


2019 ◽  
Vol 212 ◽  
pp. 162-174 ◽  
Author(s):  
Kabiruddin Khan ◽  
Diego Baderna ◽  
Claudia Cappelli ◽  
Cosimo Toma ◽  
Anna Lombardo ◽  
...  

2020 ◽  
Vol 715 ◽  
pp. 136816 ◽  
Author(s):  
Yu Huang ◽  
Tiantian Li ◽  
Shanshan Zheng ◽  
Lingyun Fan ◽  
Limin Su ◽  
...  

2013 ◽  
Vol 726-731 ◽  
pp. 175-178
Author(s):  
Zhi Min Cao ◽  
Zhen Zhen Wu ◽  
Zhi Fen Lin

There is an essential need to use computation-based quantitative structureactivity relationship (QSAR) modeling for providing information about the physicochemical properties of chemicals and their environmental fate as well as their human health effects. The major aims of this paper is to explore ways to predict and to identify hazardous combinations of chemicals relevant to humans and the environment. So we use QSAR modeling for toxicological predictions determine the potential adverse effects of reactive organic compounds in risk assessment.


2006 ◽  
Vol 14 (20) ◽  
pp. 6933-6939 ◽  
Author(s):  
Alan R. Katritzky ◽  
Minati Kuanar ◽  
Dimitar A. Dobchev ◽  
Barbara W.A. Vanhoecke ◽  
Mati Karelson ◽  
...  

2008 ◽  
Vol 16 (14) ◽  
pp. 7055-7069 ◽  
Author(s):  
Alan R. Katritzky ◽  
Svetoslav H. Slavov ◽  
Dimitar A. Dobchev ◽  
Mati Karelson

2021 ◽  
Vol 208 (05) ◽  
pp. 55-62
Author(s):  
V. Vazhev ◽  
B. Munarbaeva ◽  
N. Vazheva ◽  
M. Gubenko

Abstract. The purpose of the research is to study the possibility of predicting the fungicidal activity of a large array of organic compounds of different classes in relation to Fusarium oxysporum. Methods. The research is based on the QSAR methodology. The logarithmic form of the minimum concentration of the drug, which inhibits the visible growth of the pathogen, lgMIC, was used as an indicator of fungicidal activity. To construct predictive models, 515 compounds were selected, data on the inhibitory activity of which were obtained from the ChEMBL site. The structure of the compounds is described by molecular structure descriptors calculated by the Dragon 7 computer program; a total of 1600 descriptors are used. Calculations were performed using the PROGROC computer program (PROGgram RObustness Calculation). The algorithm of the program allows using the number of descriptors in excess of the number of substances without preliminary selection. Results. Several models were built with the size of the control sample in the range of 51–61% of the total number of compounds with the following statistical indicators: correlation coefficient R = 0,95…0,978 and standard deviation s = 0,17…0,25. When checking the validity of the models using cross-validation, the following indicators were obtained: R = 0,923 and s = 0,29, the average absolute error is 0,24.The scientific novelty of the study lies in the fact that for the first time the prediction of the fungicidal activity of a large array of organic compounds of different classes against Fusarium oxysporum was performed with high statistical indicators and with an assessment of the predictive power of the models by leave-one-out cross-checking. In the cross-validation mode, lgMIC calculations were also performed for fungicides, the experimental data on the toxicity of which with respect to Fusarium oxysporum are absent.


2011 ◽  
Vol 107 (1) ◽  
pp. 69-74 ◽  
Author(s):  
Vasyl Kovalishyn ◽  
João Aires-de-Sousa ◽  
Cristina Ventura ◽  
Ruben Elvas Leitão ◽  
Filomena Martins

2008 ◽  
Vol 25 (8) ◽  
pp. 1902-1914 ◽  
Author(s):  
Liying Zhang ◽  
Hao Zhu ◽  
Tudor I. Oprea ◽  
Alexander Golbraikh ◽  
Alexander Tropsha

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