不确定性意识深度学习和结构特征分析可用于可靠的毒性预测.
Jian-Wang Liu1, Ke-Yi Liu2, You-Chao Deng3
1Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.
Journal of chemical information and modeling
|August 19, 2025
概括
这项研究开发了先进的计算模型,用于预测药物诱导的毒性 (毒性),实现高准确性并为更安全的药物设计提供见解.
科学领域:
- 计算毒理学计算毒理学
- 药物的发现和开发.
- 药品化学 药品化学 是一个
背景情况:
- 毒性是药物开发中的一个主要安全问题,目前的预测模型缺乏精度.
- 现有的用于毒性预测的计算方法在可靠性和准确性方面存在局限性.
研究的目的:
- 建立最大的公开可用的毒性化合物的数据库.
- 开发和验证用于毒性预测的先进机器学习和深度学习模型.
- 通过不确定性量化来提高模型可靠性,并为更安全的药物设计提供见解.
主要方法:
- 编制了1831种高质量的毒性相关化合物的数据集.
- 开发了使用传统机器学习和基于图形的深度学习 (定向消息传递神经网络) 的分类模型.
- 综合不确定性量化和多尺度特征分析,用于模型验证和洞察力生成.
主要成果:
- 定向消息传递神经网络模型的平均卡帕达到70.3%.
- 不确定性量化在适用性领域内提高了预测性能,达到90.4%的Kappa.
- 与现有方法相比,开发的模型在外部测试组中表现出优异的性能.
结论:
- 开发的模型,包括不确定性量化,在预测药物诱导的毒性方面取得了重大进展.
- 多级特征分析为设计具有较低毒性潜力的更安全药物提供了可操作的见解.
- 这项工作为提高制药研究中的药物安全性和有效性建立了有价值的工具.
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