数量结构-活动关系
适用范围
计算机科学
背景(考古学)
领域(数学分析)
模型验证
数据挖掘
机器学习
人工智能
数据科学
数学
生物
数学分析
古生物学
出处
期刊:Qsar & Combinatorial Science
[Wiley]
日期:2007-05-01
卷期号:26 (5): 694-701
被引量:1641
标识
DOI:10.1002/qsar.200610151
摘要
Abstract The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance cross‐validation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes.
科研通智能强力驱动
Strongly Powered by AbleSci AI