What Counts as External Validation in QSAR Modelling across Scaffolds, Assays, Laboratories, Chemical Series, and Historical Time?

External validation is frequently treated as a binary property of quantitative structure–activity relationship models, although evaluation sets can differ from development data along chemically and experimentally distinct dimensions. A holdout may be structurally distant yet generated in the same assay and laboratory, chronologically later yet dominated by familiar chemical series, or externally sourced while retaining substantial analogue overlap. This methodological standards article proposes a multiaxial framework that distinguishes structural-distance, scaffold, chemical-series, assay, protocol, laboratory, temporal, and population or use-context externality. These dimensions are combined with cross-cutting requirements for provenance integrity, leakage control, calibration, predictive uncertainty, representation stability, and claim traceability. The framework introduces claim–validation alignment, non-compensation, weakest-relevant-dimension, provenance-break, prospective-lock, recalibration, and failure-localization rules. Minimum external-validation standards are defined around transparent split construction, independent data generation where relevant, comparison with less demanding partitions, external-set calibration and uncertainty assessment, subgroup analysis, and explicit restriction of the resulting claim. Stress tests and negative controls are positioned as mechanisms for revealing hidden dependence, leakage, activity-cliff sensitivity, and instability under reasonable analytical alternatives. A reporting checklist is proposed to make each claimed form of externality auditable without converting reporting completeness into a guarantee of validity. The contribution is an original conceptual synthesis rather than an empirically validated scoring system. It requires prospective and interlaboratory evaluation, comparison across endpoints and chemical programs, and investigation of how validation dimensions interact. It does not establish causal explanation, clinical benefit, regulatory acceptance, universal applicability, or deployment readiness.