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Prediction of soil adsorption coefficient based on deep recursive neural network

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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.

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Authors and Affiliations

  1. School of Software, Xinjiang University, Urumqi, 830008, China

    Xinyu Shi, Shengwei Tian & Shuangyin Gao

  2. Network Center, Xinjiang University, Urumqi, 830046, China

    Long Yu

  3. College of Engineering, Xinjiang Medical University, Xinjiang Uygur Autonomous Region, Urumqi, 830011, China

    Li Li

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  1. Xinyu Shi
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  2. Shengwei Tian
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  3. Long Yu
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  4. Li Li
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  5. Shuangyin Gao
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Correspondence to Shengwei Tian.

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Shi, X., Tian, S., Yu, L. et al. Prediction of soil adsorption coefficient based on deep recursive neural network. Aut. Control Comp. Sci. 51, 321–330 (2017). https://doi.org/10.3103/S0146411617050066

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