基于MTDNN的抗肿瘤药物作用机制分析

Jun Yang
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引用次数: 0

摘要

蛋白激酶是癌基因的主要依赖靶点,抗肿瘤药物主要靶向激酶。为了分析抗肿瘤药物的作用机制,构建了基于多任务深度神经网络的小分子激酶预测模型,预测小分子激酶抑制剂与激酶的相互作用。实验结果表明,基于MTDNN的小分子激酶预测模型具有良好的预测能力。不同测试集的平均auroc为0.7425。可以有效预测和分析各种激酶的治疗靶向活性,深入分析抗肿瘤药物的作用机制。将人工智能技术引入抗肿瘤药物的机理研究中,为研发精准、有针对性的抗肿瘤新药提供了参考,对于提高药物研发领域的效率和质量具有重要的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanism Analysis of Antitumor Drugs based on MTDNN
Protein kinases are the main dependent targets of oncogenes, and antitumor drugs mainly target kinases. In order to analyze the mechanism of antitumor drugs, a small molecule kinase prediction model was constructed based on multi task deep neural network to predict the interaction between small molecule kinase inhibitors and kinases. The experimental results show that the small molecule kinase prediction model based on MTDNN has good prediction ability. The average auroc on different test sets is 0.7425. It can effectively predict and analyze the therapeutic targeting activities of various kinases and deeply analyze the action mechanism of antitumor drugs. The introduction of artificial intelligence technology into the mechanism research of antitumor drugs provides a reference for the research and development of accurate and targeted new antitumor drugs, and has important practical value for improving the efficiency and quality in the field of drug research and development.
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