Journal
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
Volume 190, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ecoenv.2019.110146
Keywords
General regression neural network; Molecular descriptor; Structure-property relationship; Tetrahymena pyriformis; Toxicity
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Funding
- Scientific Research Fund of Hunan Provincial Education Department [16A047]
- Open Project Program of State Key Laboratory of Chemo/Biosensing and Chemometrics (Hunan University) [2016013]
- Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration (Hunan Institute of Engineering) [2018KF11]
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A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R-2 of 0.86 and root mean square (rms) error of 0.41, and the test set has R-2 of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.
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