Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification

Chemical synthesis planning is a
key aspect in many fields of chemistry,
especially drug discovery. Recent implementations of machine learning
and artificial intelligence techniques for retrosynthetic analysis
have shown great potential to improve computational methods for synthesis
planning. Herein, we present a multiscale, data-driven approach for
retrosynthetic analysis with deep highway networks (DHN). We automatically
extracted reaction rules (i.e., ways in which a molecule is produced)
from a data set consisting of chemical reactions derived from U.S.
patents. We performed the retrosynthetic reaction prediction task
in two steps: first, we built a DHN model to predict which group of
reactions (consisting of chemically similar reaction rules) was employed
to produce a molecule. Once a reaction group was identified, a DHN
trained on the subset of reactions within the identified reaction
group, was employed to predict the transformation rule used to produce
a molecule. To validate our approach, we predicted the first retrosynthetic
reaction step for 40 approved drugs using our multiscale model and
compared its predictive performance with a conventional model trained
on all machine-extracted reaction rules employed as a control. Our
multiscale approach showed a success rate of 82.9% at generating valid
reactants from retrosynthetic reaction predictions. Comparatively,
the control model trained on all machine-extracted reaction rules
yielded a success rate of 58.5% on the validation set of 40 pharmaceutical
molecules, indicating a significant statistical improvement with our
approach to match known first synthetic reaction of the tested drugs
in this study. While our multiscale approach was unable to outperform
state-of-the-art rule-based systems curated by expert chemists, multiscale
classification represents a marked enhancement in retrosynthetic analysis
and can be easily adapted for use in a range of artificial intelligence
strategies.