Quantitative structure–activity relationship (QSAR) modelling commonly assumes that reported bioactivity values represent comparable numerical observations. However, experimental potency records frequently contain inequality-qualified measurements, assay detection limits, threshold-dependent inactivity labels, or missing endpoints. Conventional preprocessing may exclude these observations, replace their bounds with numerical values, or convert them into binary classes, thereby changing the information available for learning. This methodological article develops a boundary-condition extension of conventional QSAR reasoning by examining when scalar-response assumptions become inadequate and how incomplete measurements can retain inferential value. Drawing on published bioactivity database, assay-curation, censored-regression, uncertainty-modelling, and molecular prediction research, the analysis distinguishes experimental observability from molecular potency and pharmacological inactivity. A censoring-aware approach is developed around explicit observation operators, preserved inequality direction, assay-context information, likelihood contributions for bounded responses, and separation of measurement uncertainty from model uncertainty. Its applicability depends on reliable endpoint definitions, identifiable measurement limits, sufficiently compatible assays, and defensible assumptions about the observation process. Illustrative comparisons are proposed to distinguish gains from retained censoring information from improvements attributable to assay curation, model complexity, or data selection. The resulting contribution specifies when incomplete potency records support quantitative learning, when they justify only constrained classification, and when their interpretation remains unresolved. Censored observations should therefore be represented according to the information established by the experiment, with predictive advantages treated as hypotheses requiring comparative validation.
IJPRAS
