Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods
10.11665/j.issn.1000-5048.2023033103
- VernacularTitle:基于深度学习和多种机器学习算法预测人体细胞色素P450抑制剂活性
- Author:
Mingde LIN
1
;
Weijie HAN
;
Xiaohe XU
;
Xiaowen DAI
;
Yadong CHEN
Author Information
1. 中国药科大学理学院
- Publication Type:Journal Article
- Keywords:
cytochrome P450;
machine learning;
deep learning;
CatBoost
- From:
Journal of China Pharmaceutical University
2023;54(3):333-343
- CountryChina
- Language:Chinese
-
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.
