Citation: Peizhe Sun, Huixin Ma, Shangyu Li, Hong Yao, Ruochun Zhang. Prediction of second-order rate constants between carbonate radical and organics by deep neural network combined with molecular fingerprints[J]. Chinese Chemical Letters, ;2022, 33(1): 438-441. doi: 10.1016/j.cclet.2021.06.061 shu

Prediction of second-order rate constants between carbonate radical and organics by deep neural network combined with molecular fingerprints

Peizhe Sun ^a ,
Huixin Ma ^a ,
Shangyu Li ^b ,
Hong Yao ^{c, *,} ,
Ruochun Zhang ^{d, e, **,}

a.
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
b.
School of Civil Engineering, Tianjin University, Tianjin 300072, China
c.
Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, Department of Municipal and Environmental Engineering, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
d.
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
e.
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin 300072, China

^**

yaohongts@163.com

zhangruochun@tju.edu.cn

Received Date: 12 April 2021
Revised Date: 23 April 2021
Accepted Date: 23 June 2021
Available Online: 29 June 2021

Figures(2)

Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants (PCs). However, reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined, and quantitative structural-activity relationships (QSARs) have not been established for rate estimation. This study applied MaxMin data processing method and used molecular fingerprints (MF) as the input of a deep neural network (DNN) to predict the rate constants between carbonate radical and organic compounds. MF parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate prediction. The vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best performance. The optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods, including random data splitting, molecular descriptors, supporting vector machine, decision tree, etc. Results showed that the MF-DNN model out-performed the other methods by more than 10% increase in prediction accuracy. Applying this MF-DNN model, we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine (1576) and veterinary practice (390). Among them, 46 drugs were identified as fast-reacting compounds, suggesting the important relations of their environmental fate with carbonate radical.
- Deep neural network,
- Carbonate radical,
- Molecular fingerprints,
- QSAR,
- Pharmaceuticals
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