When Colloids Look Like Leads: Preventing Aggregation‑Driven False Discovery in Computational and Experimental Screening of Natural Product Compounds

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Natural products occupy chemically rich space but can also present physicochemical behaviors that complicate screening interpretation. Among these, concentration-dependent colloidal self-assembly can transform an apparently potent molecular hit into a particle-mediated assay signal. This methodological framework examines aggregation as a false-discovery mechanism that spans experimental screening, virtual prioritization, and machine-learning data construction. The analysis distinguishes molecular structure from emergent colloidal state, apparent inhibition from target-specific pharmacology, colloid formation from precipitation or low free solubility, and rejection of an unsupported mechanism from rejection of the underlying scaffold. Evidence from biochemical and cellular screening, natural-product-derived compounds, biophysical studies, aggregation-specific machine learning, assay-liability prediction, and bioactivity-data curation is integrated into a proposed falsification-first strategy. The central proposal is that aggregation liability should enter discovery before mechanistic interpretation, computational flags should modify uncertainty and the next experiment, while condition-matched physical-state measurements and orthogonal counter-screens should test whether particle formation plausibly explains activity. A predicted aggregator is therefore not automatically rejected, and an experimentally observed aggregate is not automatically biologically irrelevant. Rescue through formulation represents a separate development hypothesis that must not retroactively validate an initial target claim. The framework is intended to reduce propagation of aggregation-contaminated activity labels into ranking systems and training sets while preserving chemically unusual candidates that survive falsification. The principal limitation is that the integrated workflow has not been prospectively validated across diverse natural-product libraries, assay formats, or model domains.