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Abstract

Phenol derivatives are well known by their toxicological effect on environment and human health. However, like most chemicals, the experimental determination of phenols toxicity is a very demanding and complex task. Hence, there is a great motivation for the development of quantitative structure-cytotoxicity relationship (QSCR) that can predict accurately the cytotoxicity of new phenolic compounds. Machine learning (ML) provides a set of methods that exhibit high prediction accuracy and outperforms many other approaches. In the current work, deep neural networks techniques were used to perform QSCR for a set of substituted phenol compounds. Both electron-releasing substituents and electron-withdrawing substituents were considered showing the impact of the electronic effect on the model establishment. The model established was highly successful in predicting cytotoxicity of new compounds.

Keywords

Phenols Cytotoxicity Machine learning Deep neural networks Predictive ability 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Regional Center for Training and Education Marrakech-SafiMarrakechMorocco

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