  Aims. With the large amount of molecular emission data from (sub)millimeter
observatories and incoming James Webb Space Telescope infrared spectroscopy,
access to fast forward models of the chemical composition of protoplanetary
disks is of paramount importance.
  Methods. We used a thermo-chemical modeling code to generate a diverse
population of protoplanetary disk models. We trained a K-nearest neighbors
(KNN) regressor to instantly predict the chemistry of other disk models.
  Results. We show that it is possible to accurately reproduce chemistry using
just a small subset of physical conditions, thanks to correlations between the
local physical conditions in adopted protoplanetary disk models. We discuss the
uncertainties and limitations of this method.
  Conclusions. The proposed method can be used for Bayesian fitting of the line
emission data to retrieve disk properties from observations. We present a
pipeline for reproducing the same approach on other disk chemical model sets.
