Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes

The widespread legalization of Cannabis has opened the industry to using
contemporary analytical techniques for chemotype analysis. Chemotypic data
has been collected on a large variety of oil profiles inherent to the
cultivars that are commercially available. The unknown gene regulation and
pharmacokinetics of dozens of cannabinoids offer opportunities of high
interest in pharmacology research.  Retailers in many medical and
recreational jurisdictions are typically required to report chemical
concentrations of at least some cannabinoids. Commercial cannabis
laboratories have collected large chemotype datasets of diverse Cannabis
cultivars. In this work a data set of 17,600 cultivars tested by Steep
Hill Inc., is examined using machine learning techniques to interpolate
missing chemotype observations and cluster cultivars into groups based on
chemotype similarity.   The results indicate cultivars
cluster based on their chemotypes, and that some imputation methods work
better than others at grouping these cultivars based on chemotypic
identity. Due to the missing data and to the low signal to noise ratio for
some less common cannabinoids, their behavior could not be accurately
predicted. These findings have implications for characterizing complex
interactions in cannabinoid biosynthesis and improving phenotypical
classification of Cannabis cultivars.