Principles of QSAR models validation: internal and external

TLDR

The EU REACH policy has spurred the development of general validation principles for QSAR models used in chemical regulation. The paper analyzes key QSAR validation principles, including algorithm clarity, applicability domain, and statistical validation. The authors highlight reproducibility concerns, critique common internal validation techniques for MLR, and demonstrate that only models validated externally after internal checks are reliable for regulatory use.

Abstract

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.