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Updated: Jun 11, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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药物诱导的Torsadogenicity预测模型:一种可解释的机器学习驱动的定量结构毒性关系方法

Feyza Kelleci Çelik1, Seyyide Doğan2, Gül Karaduman3

  • 1Karamanoğlu Mehmetbey University, Vocational School of Health Services, 70200, Karaman, Turkey.

Computers in biology and medicine
|September 27, 2024
PubMed
概括

这项研究引入了一种新的in silico定量结构-毒性关系 (QSTR) 模型,用于预测药物诱导的Torsade de Pointes (TdP) 风险. 该模型使用分子结构来提高早期药物安全性评估并减少动物试验.

关键词:
心脏有毒性 - 心脏有毒性机器学习 机器学习在QSAR中使用QSAR.在QSTR中,QSTR是QSTR.谢普利添加剂的解释在Torsade de pointes的位置上

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