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Automatic Control and Computer Sciences

, Volume 51, Issue 5, pp 321–330 | Cite as

Prediction of soil adsorption coefficient based on deep recursive neural network

  • Xinyu Shi
  • Shengwei Tian
  • Long Yu
  • Li Li
  • Shuangyin Gao
Article
  • 31 Downloads

Abstract

It is expensive and time consuming to measure soil adsorption coefficient (logKoc) of compounds using traditional methods, and some existing models show lower accuracies. To solve these problems, a deep learning (DL) method based on undirected graph recursive neural network (UG-RNN) is proposed in this paper. Firstly, the structures of molecules are represented by directed acyclic graphs (DAG) using RNN model; after that when a number of such neural networks are bundled together, they form a multi-level and weight sharing deep neural network to extract the features of molecules; Third, logKoc values of compounds have been predicted using back-propagation neural network. The experimental results show that the UG-RNN model achieves a better prediction effect than some shallow models. After five-fold cross validation, the root mean square error (RMSE) value is 0.46, the average absolute error (AAE) value is 0.35, and the square correlation coefficient (R2) value is 0.86.

Keywords

deep learning recursive neural network logKoc Pearson correlation coefficient molecular descriptors 

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Copyright information

© Allerton Press, Inc. 2017

Authors and Affiliations

  • Xinyu Shi
    • 1
  • Shengwei Tian
    • 1
  • Long Yu
    • 2
  • Li Li
    • 3
  • Shuangyin Gao
    • 1
  1. 1.School of SoftwareXinjiang UniversityUrumqiChina
  2. 2.Network CenterXinjiang UniversityUrumqiChina
  3. 3.College of Engineering, Xinjiang Medical UniversityXinjiang Uygur Autonomous RegionUrumqiChina

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