Study on IC50 prediction of targeted drugs for hepatoma cells based on SSA-BP neural network

Peng Liu; Huishuang Xing; Shengxian Cao; Siyuan Fan; Tianyi Sun

doi:10.1117/12.2641052

13 October 2022 Study on IC₅₀ prediction of targeted drugs for hepatoma cells based on SSA-BP neural network

Peng Liu, Huishuang Xing, Shengxian Cao, Siyuan Fan, Tianyi Sun

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228720 (2022) https://doi.org/10.1117/12.2641052
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

As a new treatment for malignant tumors, highly specific targeted drugs have been vigorously promoted in modern medical cancer treatment. At present, the problems of large single dose, high quality standard and high cost of targeted drugs have become well-known rich drugs. Accurate IC₅₀ prediction of targeted drugs has important practical significance for cancer economy and precision treatment. In this paper, 179 groups of characteristic parameters of targeted drugs against hepatoma cell line from Genomics of Drug Sensitivity in Cancer (GDSC) open source database are selected as model input. Half maximal inhibitor concentration (IC₅₀) of targeted drug action is used as model output. Then, IC50 prediction model based on SSA-BP neural network is established. Compared with other methods, the proposed model has faster convergence speed and higher prediction accuracy, and the overall regression coefficient is 0.96463.

Citation Download Citation

Peng Liu, Huishuang Xing, Shengxian Cao, Siyuan Fan, and Tianyi Sun "Study on IC₅₀ prediction of targeted drugs for hepatoma cells based on SSA-BP neural network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228720 (13 October 2022); https://doi.org/10.1117/12.2641052

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