Activities Prediction of Drug Molecules by Using the Optimal Ensemble Based on Uniform Design

  • Authors:
  • Yue Liu;Yafeng Yin;Zaixia Teng;Qi Wu;Guozheng Li

  • Affiliations:
  • School of Computer Engineering & Science, Shanghai University, Shanghai, China 200072;School of Computer Engineering & Science, Shanghai University, Shanghai, China 200072;School of Computer Engineering & Science, Shanghai University, Shanghai, China 200072;School of Computer Engineering & Science, Shanghai University, Shanghai, China 200072;School of Computer Engineering & Science, Shanghai University, Shanghai, China 200072

  • Venue:
  • ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
  • Year:
  • 2008

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Abstract

Neural network ensemble is a powerful tool for simulating the quantitative structure activity relationship in drug discovery because of its high generalization ability. However, the architecture of the ensemble and the training parameters of individual neural networks are closely relative to the generalization performance of the ensemble and the convenience of the creation of the ensemble. This paper proposes a novel creation algorithm for neural network ensemble, which employs uniform design to guide users to design the ensemble architecture and adjust the training parameters of individual neural networks. In addition, this algorithm is applied to produce neural network ensemble for predicting activities of drug molecules, which is a convenient way to achieve better results than commonly used bagging methods.