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We propose to apply the TESS algorithm to each constituent compound of Kampo medicine.
The 4th layer shows the target proteins (e.g., "ADRA1D") that are known to interact with the constituent compound ("Methylephedrine" in this case).
An example of the output page of the query "shikonin" (a constituent compound of "Lithospermum erythrorhizon") with the docking simulation option in the "Target prediction" component.
Second, we select diseases associated with the matched target protein, and link the query constituent compound to the selected diseases via the matched target protein.
First, we take a target protein of the query constituent compound and look for the same target protein in the disease target association set.
An example of the output page of the query "Sinomenine" (a constituent compound of "boiogito") with the machine learning option in the "Target prediction" component.
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Then, the predicted compound protein pairs are grouped into Kampo medicines based on their constituent compounds.
In our future versions, we will add more Kampo medicines, crude drugs, constituent compounds, and diseases.
In Japanese traditional medicines, various kinds of Kampo medicines, crude drugs, and constituent compounds exist.
In this study, Kampo medicines were associated with all possible target proteins through their constituent compounds.
Researchers still don't understand how tea's constituent compounds work together.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com