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Updated: May 5, 2026

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计数指纹增强图形神经网络模型能够精确选来自不同类别化学品的hERG阻断剂

Yuxuan Zhang1, Yuwei Liu1, Zijun Xiao1

  • 1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

Environmental science & technology
|March 9, 2026
PubMed
概括

一个新的基于双特征的神经网络 (DFNN) 模型有效地选人类以太基因相关基因 (hERG) 通道阻断剂. 这种先进的计算方法可以提高预测准确度,用于识别可能导致致命心律失常的化学物质.

关键词:
适用性领域适用性领域.心脏有毒性心脏有毒性计算毒理学模型的计算毒理学模型深度学习是一种深度学习.融合的特征是融合的特征.希尔格 (HERG) 是一个

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Many fundamental cell functions such as muscle contraction and nerve transmission rely on the electrical signals produced by the movement of positively and negatively charged ions across the cell membrane. One competent method to record current flowing across the whole cell or single ion channel is the patch-clamp technique.
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