iDILI-MT:使用多头变压器识别药物诱导的肝损伤化合物
1College of Medical Nursing, Minxi Vocational and Technical College, Longyan, Fujian, China.
PeerJ. Computer science
|September 24, 2025
概括
一个新的计算工具iDILI-MT准确地预测化合物中药物诱导性肝损伤 (DILI) 风险. 这种人工智能驱动的方法提高了早期的药物安全性评估,减少了潜在的后期失败.
科学领域:
- 计算毒理学计算毒理学
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物诱导性肝损伤 (DILI) 是药物开发失败的主要原因之一.
- 早期识别肝毒性化合物对于药物安全性和效率至关重要.
研究的目的:
- 开发和验证iDILI-MT,这是一个用于预测DILI风险的新型计算框架.
- 评估iDILI-MT的性能与现有的机器学习和图形神经网络模型相比.
主要方法:
- iDILI-MT集成了顺序特征提取,多头变压器编码和注意力机制.
- 该框架对1919种小分子化合物进行了评估.
- 使用注意力重量分析来识别肝毒性相关的关键化学动机.
主要成果:
- iDILI-MT实现了高预测性能,其AUC-ROC为0.8499和AUC-PR为0.8905.
- 该模型的性能优于传统的机器学习分类器和最先进的图形神经网络.
- 注意力分析突出了与DILI相关的特定基层结构特征.
结论:
- iDILI-MT提供了一种强大的计算方法,用于早期检测DILI风险.
- 该框架可以加速药物安全性评估,并降低药物开发中的消耗率.
- 这种人工智能驱动的方法有助于识别更安全的候选药物.
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