Machine learning for potion development at Hogwarts
Christoph F. Kurz Adriana N. König
Objective: To determine whether machine learning methods can generate useful
potion recipes for research and teaching at Hogwarts School of Witchcraft and
Wizardry. Design: Using deep neural networks to classify generated recipes into
a standard drug classification system. Setting: Hogwarts School of Witchcraft
and Wizardry. Data sources: 72 potion recipes from the Hogwarts curriculum,
extracted from the Harry Potter Wiki. Results: Most generated recipes fall into
the categories of psychoanaleptics and dermatologicals. The number of recipes
predicted for each category reflected the number of training recipes. Predicted
probabilities were often above 90% but some recipes were classified into 2 or
more categories with similar probabilities which complicates anticipating the
predicted effects. Conclusions: Machine learning powered methods are able to
generate potentially useful potion recipes for teaching and research at
Hogwarts. This corresponds to similar efforts in the non-magical world where
such methods have been applied to identify potentially effective drug
combinations.