Prediction of the antioxidant response elements' response of compound by deep learning

Fang Bai, Ding Hong, Yingying Lu, Huanxiang Liu, Cunlu Xu, Xiaojun Yao

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The antioxidant response elements (AREs) play a significant role in occurrence of oxidative stress and may cause multitudinous toxicity effects in the pathogenesis of a variety of diseases. Determining if one compound can activate AREs is crucial for the assessment of potential risk of compound. Here, a series of predictive models by applying multiple deep learning algorithms including deep neural networks (DNN), convolution neural networks (CNN), recurrent neural networks (RNN), and highway networks (HN) were constructed and validated based on Tox21 challenge dataset and applied to predict whether the compounds are the activators or inactivators of AREs. The built models were evaluated by various of statistical parameters, such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and receiver operating characteristic (ROC) curve. The DNN prediction model based on fingerprint features has best prediction ability, with accuracy of 0.992, 0.914, and 0.917 for the training set, test set, and validation set, respectively. Consequently, these robust models can be adopted to predict the ARE response of molecules fast and accurately, which is of great significance for the evaluation of safety of compounds in the process of drug discovery and development.

Original languageEnglish
Article number385
JournalFrontiers in Chemistry
Volume7
Issue numberMAY
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Antioxidant response elements (AREs)
  • Deep learning
  • Machine learning
  • Prediction
  • Toxicity

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