I mapped the patent-vs-literature coverage for 24,746 plant compounds: the gap between commercial activity and published research is wider than expected [OC] Comparison

What This Visualization Shows

This data visualization displays "I mapped the patent-vs-literature coverage for 24,746 plant compounds: the gap between commercial activity and published research is wider than expected [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on A few weeks ago I posted a version of this chart with incorrect terminology. I called the gap "FTO whitespace" when it actually shows the opposite (high patent density, not freedom to operate). Thanks to the community for catching that. Here is the corrected version with proper axis labels and terminology.

The dataset is built from the USDA Dr. Duke's phytochemical database, denormalized from 16 CSV files and enriched against five public APIs: PubMed (citation counts), [ClinicalTrials.gov](http://ClinicalTrials.gov) (study registrations), ChEMBL (bioactivity measurements), USPTO PatentsView (patent grants since 2020), and PubChem (molecular identifiers).

What the chart shows:

Each dot is one of 24,746 unique compounds. The x-axis is PubMed mentions (log scale, original values shown), the y-axis is USPTO patent grants since 2020 (same scale). Red dots are compounds in the "Patent-Literature Gap": six or more patents but fewer than 50 published studies.

30 compounds fall into this zone. They are being commercially patented but have almost no peer-reviewed pharmacological characterization. For anyone doing target selection or competitive landscape analysis in botanical drug discovery, this is the signal that matters.

Source: Ethno-API v2.3, 76,907 compound-plant records. Full methodology and a 400-record sample are on GitHub.

[github.com/wirthal1990-tech/USDA-Phytochemical-Database-JSON](http://github.com/wirthal1990-tech/USDA-Phytochemical-Database-JSON)

[ethno-api.com](http://ethno-api.com), which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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Significance and Applications

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