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Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches

  • In, Young-Yong ;
  • Lee, Sung-Kwang ;
  • Kim, Pil-Je ;
  • No, Kyoung-Tai
  • Received : 2011.11.22
  • Accepted : 2011.12.20
  • Published : 2012.02.20

Abstract

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.

Keywords

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

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  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