Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods

LIN Mingde; HAN Weijie; XU Xiaohe; DAI Xiaowen; CHEN Yadong

doi:10.11665/j.issn.1000-5048.2023033103

Journal of China Pharmaceutical University > 2023 > 54(3): 333-343. > DOI: 10.11665/j.issn.1000-5048.2023033103

LIN Mingde, HAN Weijie, XU Xiaohe, DAI Xiaowen, CHEN Yadong. Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods[J]. Journal of China Pharmaceutical University, 2023, 54(3): 333-343. DOI: 10.11665/j.issn.1000-5048.2023033103

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Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods

Institute of Medical Big Data and Artificial Intelligence, School of Science, China Pharmaceutical University, Nanjing 211198, China

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Received Date: March 30, 2023
Revised Date: June 12, 2023

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Abstract

Abstract

Inhibition of human cytochrome P450 (CYP) can lead to drug-drug interactions, resulting in serious adverse reactions.It is therefore crucial to accurately predict the inhibitory power of a given compound against a particular CYP isoform.This study compared 11 machine learning methods and 2 deep learning models based on different molecular representations.The experimental results showed that the CatBoost machine learning model based on RDKit_2d+Morgan outperformed other models in terms of accuracy and Mathews coefficient, and even outperformed previously published models.Moreover, the experimental results also showed that the CatBoost model not only had superior performance, but also consumed less computational resources.Finally, this study combined the top 3 performing models as co_model, which slightly outperformed the CatBoost model alone in terms of performance.
- cytochrome P450,
- machine learning,
- deep learning,
- CatBoost

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References

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