Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning.

Kongyang Zhu, Chao Shen, Chen Tang, Yixi Zhou, Chengyong He, Zhenghong Zuo

Chemosphere 2021 Feb

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The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management. Copyright © 2020 Elsevier Ltd. All rights reserved.

Citation

Kongyang Zhu, Chao Shen, Chen Tang, Yixi Zhou, Chengyong He, Zhenghong Zuo. Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning. Chemosphere. 2021 Feb;265:129099