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Updated: Jun 14, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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综合性肝毒性预测:集成模型整合机器学习和深度学习.

Muhammad Zafar Irshad Khan¹, Jia-Nan Ren¹, Cheng Cao^1,2

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

Frontiers in pharmacology

|September 5, 2024

概括

这项研究开发了一个整体模型来预测化学诱导的肝损伤,达到80.26%的准确性. 这种计算方法通过有效地识别潜在的肝毒性风险,有助于早期药物开发.

关键词:

深度学习是一种深度学习.组合模型组合模型组合模型肝毒性肝毒性肝毒性机器学习是机器学习.分子指纹,分子指纹.

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科学领域:

计算毒理学计算毒理学
药物的发现和开发.
在药理学中的机器学习.

背景情况:

化学物质可以导致急性肝损伤,这是一个重大的健康问题.
评估化合物安全性是复杂和昂贵的.
在 silico 方法可以早期识别候选药物的风险,减少开发费用.

研究的目的:

开发准确的定量结构-活性关系 (QSAR) 模型来预测化学肝毒性.
整合机器学习 (ML) 和深度学习 (DL) 算法,以提高预测准确度.
通过早期识别肝损伤风险,减轻药物开发成本.

主要方法:

开发QSAR模型用于使用合并策略预测肝毒性.
集成各种ML和DL算法与各种分子描述符和指纹.
利用特征选择和混合组合方法进行模型优化.
在2588种化学品和药物的数据集上训练模型,分为训练 (80%) 和测试 (20%) 集.

主要成果:

投票组合分类器实现了最佳性能,预测准确率为80.26%,AUC为82.84%,回忆率超过93%.
装袋和堆叠等组合方法也表现出强的表现.
该模型通过外部验证和交叉验证证明了与现有公布模型相比更高的可靠性.

结论:

拟议的整体模型为预测化学诱导的肝损伤提供了一种可靠和高性能的方法.
这种方法提供了对化学品和药物的肝毒性风险的可靠评估.
促进药物开发的早期风险评估.