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Updated: Jul 8, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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一种基于序列相互作用信息挖掘的深度学习方法,用于药物向 afinity 预测

Mingjian Jiang1, Yunchang Shao1, Yuanyuan Zhang1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.

PeerJ
|December 15, 2023
PubMed
概括

KC-DTA是一种新的深度学习方法,通过分析目标序列和分子图,准确地预测药物向亲和力 (DTA). 这种方法加快了in silico药物发现,减少了对昂贵的湿实验室实验的需求.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.药物目标亲和力预测的预测.图表神经网络的神经网络蛋白质的序列 蛋白质的序列

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