定义数据集 定义QSAR索赔的定义
Manal A Nael1,2, Laxman M Alakonda2, Khaled M Elokely2
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tanta University, Tanta 31527, Egypt.
Journal of chemical information and modeling
|February 27, 2026
概括
我们引入数据集合同,以提高机器学习在定量结构-活动关系 (QSAR) 建模中的可靠性. 这些可审计的文件确保了化学加工,终点定义和可重复结果的评估方法的透明报告.
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
- 机器学习 机器学习
背景情况:
- 机器学习显著推进了定量结构-活动关系 (QSAR) 建模.
- 然而,化学表征,终点定义和评估设计中的关键方法选择通常记录不良.
- 不一致的标准化和数据泄漏会使性能指标膨胀,掩盖现实世界的适用性弱点.
研究的目的:
- 提出一个标准化的框架,用于记录和验证QSAR建模方法.
- 提高化学信息学中预测性声明的透明度,可复制性和可信度.
- 将重点从模型架构转移到可验证的数据处理和评估协议.
主要方法:
- 数据集合同的引入:可执行和可审计的文件.
- 化学加工规则,终点定义和聚合逻辑的明确声明.
- 包括针对特定预测场景量身定制的数据分割和泄漏诊断.
主要成果:
- 使用当前的开源工具,数据集合同是可行的.
- 拟议的框架促进了透明和可重复的QSAR建模.
- 这种方法解决了由于不一致的做法而导致绩效指标的通货膨胀.
结论:
- 数据集合同为可审计和可信赖的QSAR建模提供了强大的解决方案.
- 该方法促进了在机器学习应用中转向可验证的科学主张的范式转变.
- 通过这些合同,将提高QSAR模型在实际应用中的可靠性.
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