Prediction of soil adsorption coefficient based on deep recursive neural network
- Authors: Shi X.1, Tian S.1, Yu L.2, Li L.3, Gao S.1
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Affiliations:
- School of Software
- Network Center
- College of Engineering, Xinjiang Medical University
- Issue: Vol 51, No 5 (2017)
- Pages: 321-330
- Section: Article
- URL: https://journals.rcsi.science/0146-4116/article/view/174935
- DOI: https://doi.org/10.3103/S0146411617050066
- ID: 174935
Cite item
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.
About the authors
Xinyu Shi
School of Software
Email: tianshengwei@163.com
China, Urumqi, 830008
Shengwei Tian
School of Software
Author for correspondence.
Email: tianshengwei@163.com
China, Urumqi, 830008
Long Yu
Network Center
Email: tianshengwei@163.com
China, Urumqi, 830046
Li Li
College of Engineering, Xinjiang Medical University
Email: tianshengwei@163.com
China, Urumqi, 830011
Shuangyin Gao
School of Software
Email: tianshengwei@163.com
China, Urumqi, 830008