Breast Cancer Drugs Screening Model Based on Graph Convolutional Network and Ensemble Method

Jia Li, Yun Zhao (), Guoxing Shi () and Xuewen Tan
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Jia Li: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China
Yun Zhao: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China
Guoxing Shi: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China
Xuewen Tan: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China

Mathematics, 2024, vol. 12, issue 12, 1-14

Abstract: Breast cancer is the first cancer incidence and the second cancer mortality in women. Therefore, for the life and health of breast cancer patients, the research and development of breast cancer drugs should be accelerated. In drug development, the search for compounds with good bioactivity, pharmacokinetics, and safety, including Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), has always been a time-consuming and labor-intensive process. In this paper, the relationship between the molecular descriptor and ADMET properties of compounds is studied. Aiming at the problem of composite ADMET attribute classification, a Stacking Algorithm based on Graph Convolutional Network (SA-GCN) was proposed. Firstly, feature selection was performed in the data of molecular descriptors. Then the SA-GCN is developed by integrating the advantages of ten classical classification algorithms. Finally, various performance indicators were used to conduct comparative experiments. Experiments show that the SA-GCN is superior to other classifiers in the classification performance of ADMET, and the classification accuracy is 97.6391%, 98.1450%, 94.4351%, 96.4587%, and 97.9764% compared to other classifiers. Therefore, this method can be well applied to the classification of ADMET properties of compounds and then could provide some help to screen out compounds with good biological activities.

Keywords: graph convolutional network; ensemble learning; feature selection; classification; drug screening (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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