Activity prediction of anti-cancer drug candidate ERα inhibitor
Breast cancer is the most common malignancy which threats the women's health worldwide. Studies have revealed that the estrogen receptor alpha subtype (ERα) plays an important role in breast development and is considered as an important target for breast cancer treatment. Compounds that can ant...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press (China Science Publishing & Media Ltd.)
2022-09-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
| Subjects: | |
| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2458 |
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| Summary: | Breast cancer is the most common malignancy which threats the women's health worldwide. Studies have revealed that the estrogen receptor alpha subtype (ERα) plays an important role in breast development and is considered as an important target for breast cancer treatment. Compounds that can antagonize ERα activity may be candidates for breast cancer treatment. A quantitative structure-activity relationship prediction model is proposed to predict the bioactivity of compounds that can be applied to anti-breast cancer drugs under small samples and multi-characteristic conditions. First, the descriptive statistics and multicollinearity diagnosis are performed on the information of 729 molecular descriptors of 1 974 compounds, and the random forest method is used to screen 20 significant variables with variable importance measure that is greater than 0.01. Then, a CNN-based two-dimensional feature matrix is constructed, and a Bayesian hyperparametric optimization (BHO) method is used to perform hyperparametric optimization of the Bi-LSTM model. Finally, the prediction effect of model is analyzed and evaluated. The results show that compared with the GBDT integrated learning method, the prediction effect of Mul-BHO-Bi-LSTM integrated machine learning prediction model is better, and the model error indexes MSE, NRMSE, error mean, and error std are less than 0.15, and the correlated indicators R2 and r are above 0.99, indicating that the integrated machine learning predictionmodel of Mul-BHO-Bi-LSTM has the good robustness and generalization, and the model can provide a method for the screening and design of anti-breast cancer drugs. |
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| ISSN: | 1000-2618 |