  Computer-assisted synthesis planning aims to help chemists find better
reaction pathways faster. Finding viable and short pathways from sugar
molecules to value-added chemicals can be modeled as a retrosynthesis planning
problem with a catalyst allowed. This is a crucial step in efficient biomass
conversion. The traditional computational chemistry approach to identifying
possible reaction pathways involves computing the reaction energies of hundreds
of intermediates, which is a critical bottleneck in silico reaction discovery.
Deep reinforcement learning has shown in other domains that a well-trained
agent with little or no prior human knowledge can surpass human performance.
While some effort has been made to adapt machine learning techniques to the
retrosynthesis planning problem, value-added chemical discovery presents unique
challenges. Specifically, the reaction can occur in several different sites in
a molecule, a subtle case that has never been treated in previous works. With a
more versatile formulation of the problem as a Markov decision process, we
address the problem using deep reinforcement learning techniques and present
promising preliminary results.
