What Should Molecular Models Learn from Inactive Compounds When Absence of Activity Reflects Biology, Assay Design, Exposure, or Measurement Limits?

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Molecular bioactivity models typically treat inactive compounds as a homogeneous negative class, yet inactivity can arise from multiple biologically and experimentally distinct causes. A compound may genuinely fail to engage its target, reach insufficient effective concentration, fall below detection thresholds, encounter unfavorable cellular states, or remain untested rather than being truly negative. This theory article examines the consequences of collapsing these different evidential states into a single computational label and argues that inactivity should be interpreted primarily as an observation generated under specified experimental conditions rather than as an intrinsic molecular property. Drawing on evidence from assay context, target engagement, intracellular exposure, solubility and permeability, experimental uncertainty, phenotypic state, negative sampling, and model evaluation, we develop a latent-cause framework for inactive bioactivity labels. This framework distinguishes relatively certain negative evidence from ambiguous negative evidence and unobserved activity, while preserving the possibility that genuinely inactive compounds provide valuable structural information. The proposed interpretation does not require uncertain negatives to be relabeled as active; instead, it motivates models and benchmarks that explicitly represent the provenance and confidence of negative evidence. Such treatment may improve the scientific meaning of molecular prediction and candidate deprioritization. However, the theory does not establish that any specific uncertainty-aware algorithm will outperform conventional binary learning. Its practical value therefore depends on richer assay metadata, orthogonal measurements, context-aware evaluation, and prospective validation.