Neural Network Prediction of Carcinogenicity of Diverse Organic Compounds

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  • TANABE Kazutoshi
    Department of Management Information Science, Chiba Institute of Technology
  • OHMORI Norihito
    Department of Management Information Science, Chiba Institute of Technology
  • ONO Shuichiro
    Department of Management Information Science, Chiba Institute of Technology
  • SUZUKI Takahiro
    Faculty of Economics, Toyo University
  • MATSUMOTO Takatoshi
    Institute of Multidisciplinary Research for Advanced Materials, Tohoku University
  • NAGASHIMA Umpei
    Grid Research Center, National Institute of Advanced Industrial Science and Technology
  • UESAKA Hiroyuki
    Department of Regional Science, Toyama University of International Studies

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  • ニューラルネットワークによる多種類の有機化合物の発ガン性の予測
  • ニューラル ネットワーク ニ ヨル タシュルイ ノ ユウキ カゴウブツ ノ ハツガンセイ ノ ヨソク

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Abstract

A three-layered neural network model to predict the hazards of a variety of compounds based on a quantitative structure-activity relationship was developed. The inputs were 10 principal components from 37 kinds of molecular descriptors calculated with MO programs. For the output the data used in the Predictive Toxicology Challenge (PTC) 2000-2001 contest were employed, containing 454 compounds with the carcinogenic activity of male rats. The total database of 454 compounds was split into training (144 compounds), validation (143) and test (167) sets. To solve the problems such as over-training, over-fitting and local minimum in training the neural network with the error-back-propagation algorithm, various conditions of the network such as the training cycles and neuron numbers of the intermediate layer were optimized. The optimum model showed a correct classification rate close to 74 %, higher than any of the PTC contestants.

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