在专有数据上训练的预测模型的协作开发的简单框架
Pablo Rodríguez-Belenguer1, Alexander Amberg2, Frank Bringezu3
1Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia 46026, Spain.
Journal of chemical information and modeling
|November 18, 2025
概括
这项研究引入了一种构建共享预测模型的方法,类似于AMES变异性模型,而不透露机密的化学结构. 使用这种方法创建的整体模型提高了预测准确性和化学空间覆盖率.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 毒理学 毒理学 毒理学
背景情况:
- 化学结构的保密性是开发预测模型的一个主要障碍.
- 协作药物发现和化学安全评估需要强大的预测建模.
研究的目的:
- 提出一种方法来构建和共享预测模型,同时保持数据保密性.
- 证明由共享预测模型衍生的集合模型的实用性.
主要方法:
- 一种简单的方法,可以构建和共享预测模型.
- 从多个共享模型中使用逻辑和机器学习算法开发集合模型.
- 这是一项涉及四家制药和化学公司的合作项目.
主要成果:
- 与单个模型相比,整体模型显示化学空间的覆盖率提高,预测准确度提高.
- 在AMES突变性终点预测中观察到预测质量的明显好处.
- 该方法确保不会从公司设施出口任何机密信息.
结论:
- 提出的方法提供了一种安全有效的方法来构建和共享预测模型.
- 整体建模显著提高了化学和制药研究中的预测性能.
- 该方法使用开源软件,可审计,并维护数据隐私.
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