在药物发现中数据驱动的毒性预测:当前状态和未来方向
Ningning Wang1, Xinliang Li1, Jing Xiao2
1Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; The Hunan Institute of Pharmacy Practice and Clinical Research, Changsha 410008 Hunan, PR China.
早期毒性预测对于药物发现至关重要,减少候选药物的消耗. 本综述涵盖了数据驱动的毒性预测方法,挑战和计算毒理学的未来方向.
科学领域:
- 计算毒理学和化学信息学.
- 药物的发现和开发.
- 数据科学和机器学习在药理学中的应用.
背景情况:
- 早期的毒性评估在药物发现中至关重要,显著影响候选药物消耗率.
- 信息技术的进步加速了计算毒性预测方法的发展.
- 了解和减轻药物毒性对于高效和安全的药物开发至关重要.
研究的目的:
- 为数据驱动的毒性预测提供当前情景的全面概述.
- 分析与毒性预测相关的特征和挑战.
- 审查建模方法和现场可用的工具的演变.
主要方法:
- 对数据驱动的毒性预测现有文献进行系统审查.
- 分析研究现状,挑战,并为毒性预测提出解决方案.
- 基于特征,建模方法和工具的方法的分类.
主要成果:
- 详细检查化学毒性的特征和预测化学毒性的固有困难.
- 追踪计算毒理学建模的历史发展和当前趋势.
- 目前可用的毒性预测软件和平台的识别和摘要.
结论:
- 数据驱动的毒性预测是一个快速发展的领域,具有改善药物发现的巨大潜力.
- 应对现有挑战需要在方法和工具开发方面不断创新.
- 未来的研究应该探索新的方向,以提高预测模型的准确性和适用性.
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