Ferrocene-Mediated Proton-Coupled Electron Transfer in a Series of Ferrocifen-Type Breast-Cancer Drug Candidates

2006 ◽  
Vol 118 (2) ◽  
pp. 291-296 ◽  
Author(s):  
Elizabeth Hillard ◽  
Anne Vessières ◽  
Laurent Thouin ◽  
Gérard Jaouen ◽  
Christian Amatore
Keyword(s):  
Breast Cancer ◽  
Electron Transfer ◽  
Cancer Drug ◽  
Drug Candidates ◽  
Proton Coupled Electron Transfer

Ferrocene-Mediated Proton-Coupled Electron Transfer in a Series of Ferrocifen-Type Breast-Cancer Drug Candidates

2006 ◽  
Vol 45 (2) ◽  
pp. 285-290 ◽  
Author(s):  
Elizabeth Hillard ◽  
Anne Vessières ◽  
Laurent Thouin ◽  
Gérard Jaouen ◽  
Christian Amatore
Keyword(s):  
Breast Cancer ◽  
Electron Transfer ◽  
Cancer Drug ◽  
Drug Candidates ◽  
Proton Coupled Electron Transfer

scholarly journals An Optimized Modeling Algorithm for Breast Cancer Drug Candidates Based on NSGAII

2021 ◽  
Vol 2 (3) ◽  
pp. 50-57
Author(s):  
Chenyao Fan ◽  
Huawei Mei
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Malignant Tumors ◽  
Molecular Descriptor ◽  
Classification Model ◽  
Cancer Drug ◽  
Residual Network ◽  
Drug Candidates ◽  
Starting Point ◽  
The Impact

Breast cancer is one of the most common malignant tumors in women. It seriously threatens the safety of women worldwide. It is an important and urgent task to research and develop anti-breast cancer drugs and improve the therapeutic effect of breast cancer. Taking the actual sample data as the main starting point, firstly, the prediction model of pIC50 is established by ResNet residual network and neural network (NN) to judge the biological activity. Then the classification model of ADMET property is established by ResNet residual network and LightGBM, and the model fusion is realized by Choquet fuzzy integral. Finally, the NSGAII multi-objective optimization algorithm is used to determine the range of values that each molecular descriptor obtains in the range of good biological activity, and ultimately to optimize the modeling of anti-breast cancer drug candidates. The experimental results show that the algorithm improves the prediction accuracy of biological activity, realizes the efficient and accurate classification of ADMET properties, and accurately describes the impact of molecular descriptors on biological activity.

Sign in / Sign up

Export Citation Format

Share Document