在中,使用可解释的机器学习方法预测化学急性皮肤毒性
Shang Lou1, Zhuohang Yu1, Zejun Huang1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Chemical research in toxicology
|February 21, 2024
概括
这项研究开发了机器学习模型来预测急性皮肤毒性,减少对动物试验的依赖. 这些模型准确地识别了农药和其他产品中的潜在风险,有助于安全评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 评估急性皮肤毒性对于人类安全至关重要,特别是对于农药.
- 目前用于皮肤毒性的动物试验方法有限且具有挑战性.
- 预测建模为传统动物实验提供了一个有希望的替代方案.
研究的目的:
- 开发精确的机器学习模型来预测急性皮肤毒性.
- 确定影响皮肤毒性的关键结构特征.
- 创建一个易于使用的毒性评估工具.
主要方法:
- 整合大型数据集 (1735只子,1679只老鼠) 用于模型训练.
- 利用机器学习和深度学习算法.
- 采用SARpy,Shapley添加剂解释,以及专注的FP热图来进行特征分析.
主要成果:
- 实现了高预测性能,AUC值为78.0% (子) 和82.0% (老鼠).
- 确定了与急性皮肤毒性相关的显著结构警报和碎片.
- 开发了一个独立的软件工具,用于可访问的毒性预测.
结论:
- 开发的模型为急性皮肤毒性评估提供了有效的工具.
- 这种方法支持农药,化品和药物的安全性评估.
- 这些发现有助于减少毒性研究中的动物试验.
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