Document Details

Document Type : Article In Journal 
Document Title :
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression
 
Document Language : English 
Abstract : Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity. 
ISSN : 1949-2553 
Journal Name : Oncotarget 
Volume : 8 
Issue Number : 30 
Publishing Year : 1438 AH
2017 AD 
Article Type : Article 
Added Date : Monday, July 1, 2019 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Qiang SuSu, Qiang ResearcherDoctorate 
Wencong LuLu, Wencong ResearcherDoctorate 
Dongshu DuDu, Dongshu InvestigatorDoctoratesdhzdds@163.com
Fuxue ChenChen, Fuxue InvestigatorDoctoratechemfuxue@staff.shu.edu.cn
Bing NiuNiu, Bing InvestigatorDoctoratebniu@gordonlifescience.org
Kuo-Chen ChouChou, Kuo-Chen ResearcherDoctorate 

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