Assay Interference in Natural Product Screening and Its Consequences for Lead Discovery, Bioactivity Modeling, and Candidate Prioritization

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Natural products are exceptionally rich sources of chemically diverse bioactive compounds, yet their screening outputs are frequently misinterpreted due to various technical and biological interferences that confound measured signals. This challenge has expanded beyond experimental hit confirmation, as screening data now routinely feed into bioactivity databases, machine-learning models, virtual-screening platforms, and compound-prioritization pipelines. This critical review systematically examines major sources of distortion, including optical interference, reporter perturbation, redox activity, colloidal aggregation, nonspecific reactivity, promiscuous inhibition, residual complexity, impurities, and mixture effects, all of which can compromise the evidence used to nominate natural-product leads. Furthermore, it explores how such distortions propagate into computational frameworks when critical metadata—such as assay provenance, detection technology, purity levels, counter-screening results, and orthogonal validation—are inadequately documented. The central thesis posits that an activity measurement should be treated as an evidence-bearing observation rather than an automatically mechanism-qualified label. Accordingly, the review proposes a graded distinction among apparent activity, interference-qualified activity, orthogonally supported pharmacology, and evidence-qualified lead status, while avoiding the binary assumption that all structurally suspicious or promiscuous compounds are artifactual. It also evaluates computational interference filters, assay-aware modeling, data curation, virtual-screening ranking, and validation practices as complementary safeguards. The primary implication is methodological: candidate prioritization must reflect the quality and provenance of the activity evidence underpinning computational predictions. Key limitations persist, including assay-specific behaviors, chemical-space dependencies, genuine polypharmacology, incomplete interference coverage, and the lack of prospective data quantifying ranking errors specifically attributable to assay interference.