Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 251-256.doi: 10.11896/jsjkx.200700066

• Artificial Intelligence • Previous Articles     Next Articles

Predicting Drug Molecular Properties Based on Ensembling Neural Networks Models

XIE Liang-xu1,2, LI Feng3, XIE Jian-ping4, XU Xiao-jun1   

  1. 1 Institute of Bioinformatics, Medical Engineering, School of Electrical, Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
    2 Jiangsu Sino-Israel Industrial Technology Research Institute,Changzhou,Jiangsu 213100,China
    3 School of Electrical and Information Engineering,Jiangsu University of Technology,Changzhou,Jiangsu 213001,China4 School of Science,Huzhou University,Huzhou,Zhejiang 313000,China
  • Received:2020-07-10 Revised:2020-10-20 Online:2021-09-15 Published:2021-09-10
  • About author:XIE Liang-xu,born in 1987,postgra-duate,associate professor,is a member of China Computer Federation.His main research interest includes AI aided drug design and data mining.
    XU Xiao-jun,born in 1979,professor,Jiangsu distinguished professor.His main research interest includes computational biophysics and AI aided biomolecules structure prediction.
  • Supported by:
    National Natural Science Foundation of China(12074151,22003020),Natural Science Foundation of Jiangsu Province,China(BK20191032),Changzhou Sci & Tech Program(CJ20200045) and Funding from Jiangsu Sino-Israel Industrial Technology Research Institute(JSIITRI202009)

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Abstract: Artificial intelligence (AI) methods have made great success in predicting chemical properties and bioactivity of drug molecules in the Bioinformatics field.Neural network gains wide applications in the process of drug discovery.However,the shallow neural network (SNN) gives lower accuracy while deep neural networks (DNN) are easy to be overfitting.Model ensembling is expected to further improve the predictive performance of weak learners in traditional machine learning methods.Therefore,it is the first time to apply model ensembling strategy to predict the properties of drug molecules.By encoding molecular structures,the combination strategies,averaging,and stacking methods are adopted to increase predicting accuracy of pKa of drug molecules.Compared with DNN,the stacking strategy presents the best predictive accuracy and the Pearson coefficient reaches to 0.86.Ensembling weak learners of the neural networks can reproduce the accuracy of DNN while keeping the satisfied generalization ability.The results show that ensembling method can increase the predictive accuracy and reliability.

Key words: Bioinformatics, Computer aided drug discovery, Deep learning, Machine learning, Model ensembling

CLC Number: 

  • TP183
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