[Bjonnh.net]# _

The notion of data transparency is gaining a strong awareness among the scientific community. The availability of raw data is actually regarded as a fundamental way to advance science by promoting both integrity and reproducibility of research outcomes. Particularly, in the field of natural product and chemical research, NMR spectroscopy is a fundamental tool for structural elucidation and quantification (qNMR). As such, the accessibility of original NMR data, i.e., Free Induction Decays (FIDs), fosters transparency in chemical research and optimizes both peer review and reproducibility of reports by offering the fundamental tools to perform efficient structural verification.

High-throughput biology has contributed a wealth of data on chemicals, including natural products (NPs). Recently, attention was drawn to certain, predominantly synthetic, compounds that are responsible for disproportionate percentages of hits but are false actives. Spurious bioassay interference led to their designation as pan-assay interference compounds (PAINS). NPs lack comparable scrutiny, which this study aims to rectify. Systematic mining of 80+ years of the phytochemistry and biology literature, using the NAPRALERT database, revealed that only 39 compounds represent the NPs most reported by occurrence, activity, and distinct activity.

A recent article by Baell(1) on the problems experienced by medicinal chemists with pan-assay interference compounds (PAINS) and Shoichet’s work(2) on the impact of aggregation occurring in high throughput screening libraries, prompts a consideration of how these and other similar problems are experienced by pharmacognosists with promiscuous invalid metabolites as panaceas (PIMPs). Contrary to the classical definition of secondary metabolites as being species specific (or near specific), several natural products, particularly in the more extensively investigated plant kingdom, are common across species, genera, and even families (e.