We collected the data of essential oils (essential oils) of aromatic plants by computer and artificial recognition methods, such as the types, contents, corresponding protein targets and diseases, etc., to build a volatile compounds database of aromatic plants.
We collected 1016 Chinese aromatic plant species, containing more than 17,000 data, including 4657 different volatile compounds.
There were 537 compounds with clear biological activity data, corresponding to 615 targets, and these 615 targets matched 1445 diseases.
The results of our research suggest that machine learning-driven screening of aromatic plant volatiles can be a valuable and efficient approach for identifying compounds with sleep-promoting effects.
The use of AI models can significantly reduce the time and cost involved in screening a large number of aromatic compounds.
