基于表型的药物诱导肝损伤的化学模型:从公共数据到专有数据
Mohammad Moein1, Markus Heinonen1, Natalie Mesens2
1Department of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland.
Chemical research in toxicology
|August 9, 2023
概括
本研究引入了一种使用临床前毒理学数据预测药物诱导肝损伤 (DILI) 的新方法. 增强的数据集可以提高机器学习模型对DILI预测的准确性.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 药物诱导性肝损伤 (DILI) 是一个重要的安全问题,导致药物市场退出.
- 目前的DILI预测模型通常依赖于药物标签或病例报告中的有限的二进制注释.
- 机器学习的进步为使用化学结构改善in silico DILI预测提供了潜力.
研究的目的:
- 开发一个更具信息性的数据集,用于DILI预测,使用一种基于临床前毒理学研究的新型表型注释.
- 构建和评估用于DILI预测的机器学习模型,利用这种增强的数据集.
- 评估数据源差异对模型概括的影响.
主要方法:
- 从使用INHAND注释的体内临床前毒理学研究中提取了肝毒性信息.
- 创建了430种独特化合物的数据集,其中包括各种肝病理学发现.
- 在TG-GATE数据集上使用复合指纹开发和训练DILI预测模型.
- 使用约翰逊和约翰逊公司的外部测试套件验证模型.
主要成果:
- 证明成功地预测了TG-GATE化合物的DILI标签.
- 量化了数据集差异对模型概括性能的影响.
- 强调了基于表型的注释对DILI预测的有用性.
结论:
- 新的基于表型的注释为基于机器学习的DILI预测提供了更可靠的数据集.
- 机器学习模型可以使用化学结构和临床前数据有效预测DILI.
- 了解数据集差异对于DILI预测中的强大的模型概括至关重要.
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