基于in silico建模的新替代方法来预测药物和草药诱导的肝损伤:一篇综述
Hyun Kil Shin1, Ruili Huang2, Minjun Chen3
1Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), 34114, Daejeon, Republic of Korea.
概括
新方法方法 (NAMs) 利用深度学习和不良结果途径 (AOPs) 来预测肝损伤. 这些先进的in silico模型提高了鉴定各种化合物的药物诱导肝损伤的准确性.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 肝损伤是严重和频繁的毒性终点,特别与药物和食补充剂消费有关.
- 新方法方法 (NAMs) 为预测广泛的毒性提供创新技术.
- 在模型越来越重要,用于预测化学诱导的肝损伤.
研究的目的:
- 审查最近在肝损伤预测的in silico建模方面的进展.
- 突出深度学习和不利结果途径 (AOPs) 在这个领域的应用.
- 讨论使用体外数据和基于结构的模型来预测肝毒性.
主要方法:
- 专注于深度学习算法,应用于in silico肝损伤预测.
- 在体外数据与不良结果途径 (AOPs) 的整合.
- 基于结构的模型的开发,用于预测肝脏AOPs中的分子启动事件.
主要成果:
- 先进的机器学习,特别是深度学习,显著提高了 in silico 毒性模型的准确性.
- 毒理学中的不良结果途径 (AOP) 和大数据提高了预测性能和可解释性.
- 针对药物样分子开发的模型显示可用于预测草药产品的肝毒性.
结论:
- 尽管预测肝损伤很复杂,但通过机器学习和扩展体外数据库,已经取得了重大进展.
- 结合生物知识的in silico模型对预测药物诱导的肝损伤有很大的前景.
- 计算毒理学的持续发展对于推进安全评估至关重要.
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