关于深度学习在预测性药物毒理学研究中的近期应用的综述
Krishnendu Sinha1, Nabanita Ghosh2, Parames C Sil3
1Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India.
Chemical research in toxicology
|August 10, 2023
概括
深度学习模型在预测药物毒性方面表现有前途,与传统动物模型相比,具有优势. 解决数据处理和可解释性等挑战是监管接受和改善药物开发中的患者安全的关键.
科学领域:
- 计算机毒理学和药理学
- 人工智能在药物发现中的作用
背景情况:
- 传统的药物毒性预测依赖于动物模型,引发了伦理和效率方面的担忧.
- 深度学习 (DL) 提供了先进的计算方法来预测药物毒性.
- 现有的DL方法在数据处理,解释性和监管批准方面面临挑战.
研究的目的:
- 审查用于药物毒性预测的深度学习的最新进展.
- 突出DL比传统方法的优势,并讨论局限性.
- 探索在预测毒理学中的伦理考虑和新兴应用.
主要方法:
- 对利用多种数据源 (化学结构,基因组学,选试验) 的深度学习模型的审查.
- 讨论深度学习的自动化功能工程能力.
- 分析DL和传统毒理学方法之间的整合策略.
主要成果:
- 深度学习模型在预测各种毒性结果方面表现良好.
- DL 便于从复杂的生物数据集中自动提取特征.
- 新兴应用包括预测药物相互作用和罕见的亚群毒性.
结论:
- 深度学习对于推进预测毒理学和药物安全性评估至关重要.
- 解决伦理问题和监管障碍对于DL采用至关重要.
- 将DL与传统方法相结合,可以提高药物安全性评估,加快药物发现.
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