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.
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Douali, L. (2020). Quantitative Prediction of Toxicity of Substituted Phenols Using Deep Learning. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_12
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DOI: https://doi.org/10.1007/978-3-030-21005-2_12
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