Bulletin of the Korean Chemical Society

대한화학회 (Korean Chemical Society)
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Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches

  • In, Young-Yong (Bioinformatics and Molecular Design Research Center) ;
  • Lee, Sung-Kwang (Department of Chemistry, Hannam University) ;
  • Kim, Pil-Je (National Institute of Environmental Research) ;
  • No, Kyoung-Tai (Bioinformatics and Molecular Design Research Center)
  • 투고 : 2011.11.22
  • 심사 : 2011.12.20
  • 발행 : 2012.02.20

초록

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

키워드

QSAR;Fathead minnow;Acute toxicity;Consensus model;ANN

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참고문헌

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피인용 문헌

  1. QSAR Approach for Toxicity Prediction of Chemicals Used in Electronics Industries vol.40, pp.2, 2014, https://doi.org/10.5668/JEHS.2014.40.2.105